Ibm Switch 15 Users Manual SPSS Er User’s Guide

15 to the manual a6cc1e69-0f23-4859-9bbf-38da248a72cc

2015-02-02

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IBM SPSS Modeler 15 User’s Guide

Note: Before using this information and the product it supports, read the general information
under Notices on p. 249.
This edition applies to IBM SPSS Modeler 15 and to all subsequent releases and modifications
until otherwise indicated in new editions.
Adobe product screenshot(s) reprinted with permission from Adobe Systems Incorporated.
Microsoft product screenshot(s) reprinted with permission from Microsoft Corporation.
Licensed Materials - Property of IBM
© Copyright IBM Corporation 1994, 2012.

U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP
Schedule Contract with IBM Corp.

Preface
IBM® SPSS® Modeler is the IBM Corp. enterprise-strength data mining workbench. SPSS
Modeler helps organizations to improve customer and citizen relationships through an in-depth
understanding of data. Organizations use the insight gained from SPSS Modeler to retain
profitable customers, identify cross-selling opportunities, attract new customers, detect fraud,
reduce risk, and improve government service delivery.
SPSS Modeler’s visual interface invites users to apply their specific business expertise, which
leads to more powerful predictive models and shortens time-to-solution. SPSS Modeler offers
many modeling techniques, such as prediction, classification, segmentation, and association
detection algorithms. Once models are created, IBM® SPSS® Modeler Solution Publisher
enables their delivery enterprise-wide to decision makers or to a database.

About IBM Business Analytics
IBM Business Analytics software delivers complete, consistent and accurate information that
decision-makers trust to improve business performance. A comprehensive portfolio of business
intelligence, predictive analytics, financial performance and strategy management, and analytic
applications provides clear, immediate and actionable insights into current performance and the
ability to predict future outcomes. Combined with rich industry solutions, proven practices and
professional services, organizations of every size can drive the highest productivity, confidently
automate decisions and deliver better results.
As part of this portfolio, IBM SPSS Predictive Analytics software helps organizations predict
future events and proactively act upon that insight to drive better business outcomes. Commercial,
government and academic customers worldwide rely on IBM SPSS technology as a competitive
advantage in attracting, retaining and growing customers, while reducing fraud and mitigating
risk. By incorporating IBM SPSS software into their daily operations, organizations become
predictive enterprises – able to direct and automate decisions to meet business goals and achieve
measurable competitive advantage. For further information or to reach a representative visit
http://www.ibm.com/spss.

Technical support
Technical support is available to maintenance customers. Customers may contact Technical
Support for assistance in using IBM Corp. products or for installation help for one of the
supported hardware environments. To reach Technical Support, see the IBM Corp. web site
at http://www.ibm.com/support. Be prepared to identify yourself, your organization, and your
support agreement when requesting assistance.

iii

Contents
1

About IBM SPSS Modeler

IBM SPSS Modeler Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IBM SPSS Modeler Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IBM SPSS Modeler Administration Console . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IBM SPSS Modeler Batch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IBM SPSS Modeler Solution Publisher. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
IBM SPSS Modeler Server Adapters for IBM SPSS Collaboration and Deployment Services .
IBM SPSS Modeler Editions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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IBM SPSS Modeler Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
SPSS Modeler Professional Documentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
SPSS Modeler Premium Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Application Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Demos Folder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

New Features

New and Changed Features in IBM SPSS Modeler 15 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
New features in IBM SPSS Modeler Professional . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
New features in IBM SPSS Modeler Premium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
New Nodes in This Release . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

IBM SPSS Modeler Overview

Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Starting IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Launching from the Command Line . . . . . . . . .
Connecting to IBM SPSS Modeler Server . . . .
Changing the Temp Directory . . . . . . . . . . . . . .
Starting Multiple IBM SPSS Modeler Sessions
IBM SPSS Modeler Interface at a Glance . . . . . . . .

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IBM SPSS Modeler Stream Canvas . . . . . . . . .
Nodes Palette . . . . . . . . . . . . . . . . . . . . . . . . .
IBM SPSS Modeler Managers. . . . . . . . . . . . .
IBM SPSS Modeler Projects . . . . . . . . . . . . . .
IBM SPSS Modeler Toolbar . . . . . . . . . . . . . . .
Customizing the Toolbar . . . . . . . . . . . . . . . . . .
Customizing the IBM SPSS Modeler Window. .

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Changing the icon size for a stream . . . . . . . . .
Using the Mouse in IBM SPSS Modeler . . . . . .
Using Shortcut Keys . . . . . . . . . . . . . . . . . . . .
Printing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Automating IBM SPSS Modeler . . . . . . . . . . . . . . .

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Understanding Data Mining

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Data Mining Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Assessing the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
A Strategy for Data Mining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
The CRISP-DM Process Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Types of Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
Data Mining Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

Building Streams

Stream-Building Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Building Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Working with Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Working with Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Stream Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Running Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Working with Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adding Comments and Annotations to Nodes and Streams . . .
Saving Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Loading Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Mapping Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Tips and Shortcuts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Handling Missing Values

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Overview of Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Handling Missing Values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
Handling Records with Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Handling Fields with Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Imputing or Filling Missing Values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
CLEM Functions for Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

Building CLEM Expressions

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About CLEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
CLEM Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
Values and Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
Expressions and Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Stream, Session, and SuperNode Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Working with Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
Handling Blanks and Missing Values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Working with Numbers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Working with Times and Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Summarizing Multiple Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Working with Multiple-Response Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
The Expression Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
Accessing the Expression Builder . . . . . . . . . . . . . . . .
Creating Expressions . . . . . . . . . . . . . . . . . . . . . . . . . .
Selecting Functions . . . . . . . . . . . . . . . . . . . . . . . . . . .
Selecting Fields, Parameters, and Global Variables . . .
Viewing or Selecting Values. . . . . . . . . . . . . . . . . . . . .
Checking CLEM Expressions . . . . . . . . . . . . . . . . . . . .
Find and Replace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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CLEM Language Reference

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CLEM Reference Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
CLEM Datatypes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Integers. . . . . .
Reals . . . . . . . .
Characters . . .
Strings. . . . . . .
Lists. . . . . . . . .
Fields. . . . . . . .
Dates. . . . . . . .
Time . . . . . . . .
CLEM Operators . . .

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Functions Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
Conventions in Function Descriptions .
Information Functions . . . . . . . . . . . . .
Conversion Functions . . . . . . . . . . . . .
Comparison Functions . . . . . . . . . . . . .

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Logical Functions. . . . . . . . . . . . . . . . . . . . . . .
Numeric Functions . . . . . . . . . . . . . . . . . . . . .
Trigonometric Functions . . . . . . . . . . . . . . . . .
Probability Functions . . . . . . . . . . . . . . . . . . . .
Bitwise Integer Operations . . . . . . . . . . . . . . .
Random Functions . . . . . . . . . . . . . . . . . . . . . .
String Functions. . . . . . . . . . . . . . . . . . . . . . . .
SoundEx Functions . . . . . . . . . . . . . . . . . . . . .
Date and Time Functions . . . . . . . . . . . . . . . . .
Sequence Functions . . . . . . . . . . . . . . . . . . . .
Global Functions . . . . . . . . . . . . . . . . . . . . . . .
Functions Handling Blanks and Null Values . . .
Special Fields . . . . . . . . . . . . . . . . . . . . . . . . .

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Using IBM SPSS Modeler with a Repository

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About the IBM SPSS Collaboration and Deployment Services Repository . . . . . . . . . . . . . . . . . . 158
Storing and Deploying Repository Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
Connecting to the Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Entering Credentials for the Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Browsing the Repository Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
Storing Objects in the Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
Setting Object Properties. . . . . . . . . . .
Storing Streams. . . . . . . . . . . . . . . . . .
Storing Projects. . . . . . . . . . . . . . . . . .
Storing Nodes . . . . . . . . . . . . . . . . . . .
Storing Output Objects. . . . . . . . . . . . .
Storing Models and Model Palettes . . .
Retrieving Objects from the Repository . . . .

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Choosing an Object to Retrieve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Selecting an Object Version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Searching for Objects in the Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Modifying Repository Objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Creating, Renaming, and Deleting Folders . . . .
Locking and Unlocking Repository Objects. . . .
Deleting Repository Objects. . . . . . . . . . . . . . .
Managing Properties of Repository Objects . . . . . .

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Viewing Folder Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
Viewing and Editing Object Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Managing Object Version Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

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Deploying Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
Stream Deployment Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
The Scoring Branch. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

10 Exporting to External Applications

195

About Exporting to External Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Opening a Stream in IBM SPSS Modeler Advantage. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
Importing and Exporting Models as PMML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
Model Types Supporting PMML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198

11 Projects and Reports

200

Introduction to Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200
CRISP-DM View. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Classes View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Building a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
Creating a New Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Adding to a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Transferring Projects to the IBM SPSS Collaboration and Deployment Services Repository .
Setting Project Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Annotating a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Object Properties. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Closing a Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Generating a Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Saving and Exporting Generated Reports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212

12 Customizing IBM SPSS Modeler

215

Customizing IBM SPSS Modeler Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Setting IBM SPSS Modeler Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
System Options . . . . . . . . . . . .
Setting Default Directories. . . .
Setting User Options . . . . . . . .
Setting User Information . . . . .

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Customizing the Nodes Palette . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Customizing the Palette Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Changing a Palette Tab View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
CEMI Node Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 229

13 Performance Considerations for Streams and Nodes

230

Order of Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
Node Caches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Performance: Process Nodes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Performance: Modeling Nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234
Performance: CLEM Expressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

Appendices
A Accessibility in IBM SPSS Modeler

236

Overview of Accessibility in IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
Types of Accessibility Support . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
Accessibility for the Visually Impaired .
Accessibility for Blind Users . . . . . . . .
Keyboard Accessibility . . . . . . . . . . . .
Using a Screen Reader . . . . . . . . . . . .
Tips for Use . . . . . . . . . . . . . . . . . . . . . . . .

......
......
......
......
......

....
....
....
....
....

236
237
238
245
246

Interference with Other Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
JAWS and Java . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Using Graphs in IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247

B Unicode Support

248

Unicode Support in IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248

C Notices

249

Index

252

Chapter

About IBM SPSS Modeler

IBM® SPSS® Modeler is a set of data mining tools that enable you to quickly develop predictive
models using business expertise and deploy them into business operations to improve decision
making. Designed around the industry-standard CRISP-DM model, SPSS Modeler supports the
entire data mining process, from data to better business results.
SPSS Modeler offers a variety of modeling methods taken from machine learning, artificial
intelligence, and statistics. The methods available on the Modeling palette allow you to derive
new information from your data and to develop predictive models. Each method has certain
strengths and is best suited for particular types of problems.
SPSS Modeler can be purchased as a standalone product, or used as a client in
combination with SPSS Modeler Server. A number of additional options are also
available, as summarized in the following sections. For more information, see
http://www.ibm.com/software/analytics/spss/products/modeler/.

IBM SPSS Modeler Products
The IBM® SPSS® Modeler family of products and associated software comprises the following.

IBM SPSS Modeler

IBM SPSS Modeler Server

IBM SPSS Modeler Administration Console

IBM SPSS Modeler Batch

IBM SPSS Modeler Solution Publisher

IBM SPSS Modeler Server adapters for IBM SPSS Collaboration and Deployment Services

IBM SPSS Modeler
SPSS Modeler is a functionally complete version of the product that you install and run on your
personal computer. You can run SPSS Modeler in local mode as a standalone product, or use it
in distributed mode along with IBM® SPSS® Modeler Server for improved performance on
large data sets.
With SPSS Modeler, you can build accurate predictive models quickly and intuitively, without
programming. Using the unique visual interface, you can easily visualize the data mining process.
With the support of the advanced analytics embedded in the product, you can discover previously
hidden patterns and trends in your data. You can model outcomes and understand the factors that
influence them, enabling you to take advantage of business opportunities and mitigate risks.
SPSS Modeler is available in two editions: SPSS Modeler Professional and SPSS Modeler
Premium. For more information, see the topic IBM SPSS Modeler Editions on p. 3.
© Copyright IBM Corporation 1994, 2012.

2
Chapter 1

IBM SPSS Modeler Server
SPSS Modeler uses a client/server architecture to distribute requests for resource-intensive
operations to powerful server software, resulting in faster performance on larger data sets.
SPSS Modeler Server is a separately-licensed product that runs continually in distributed analysis
mode on a server host in conjunction with one or more IBM® SPSS® Modeler installations.
In this way, SPSS Modeler Server provides superior performance on large data sets because
memory-intensive operations can be done on the server without downloading data to the client
computer. IBM® SPSS® Modeler Server also provides support for SQL optimization and
in-database modeling capabilities, delivering further benefits in performance and automation.

IBM SPSS Modeler Administration Console
The Modeler Administration Console is a graphical application for managing many of the SPSS
Modeler Server configuration options, which are also configurable by means of an options file.
The application provides a console user interface to monitor and configure your SPSS Modeler
Server installations, and is available free-of-charge to current SPSS Modeler Server customers.
The application can be installed only on Windows computers; however, it can administer a server
installed on any supported platform.

IBM SPSS Modeler Batch
While data mining is usually an interactive process, it is also possible to run SPSS Modeler
from a command line, without the need for the graphical user interface. For example, you might
have long-running or repetitive tasks that you want to perform with no user intervention. SPSS
Modeler Batch is a special version of the product that provides support for the complete analytical
capabilities of SPSS Modeler without access to the regular user interface. An SPSS Modeler
Server license is required to use SPSS Modeler Batch.

IBM SPSS Modeler Solution Publisher
SPSS Modeler Solution Publisher is a tool that enables you to create a packaged version of an
SPSS Modeler stream that can be run by an external runtime engine or embedded in an external
application. In this way, you can publish and deploy complete SPSS Modeler streams for use in
environments that do not have SPSS Modeler installed. SPSS Modeler Solution Publisher is
distributed as part of the IBM SPSS Collaboration and Deployment Services - Scoring service,
for which a separate license is required. With this license, you receive SPSS Modeler Solution
Publisher Runtime, which enables you to execute the published streams.

IBM SPSS Modeler Server Adapters for IBM SPSS Collaboration and Deployment
Services
A number of adapters for IBM® SPSS® Collaboration and Deployment Services are available that
enable SPSS Modeler and SPSS Modeler Server to interact with an IBM SPSS Collaboration and
Deployment Services repository. In this way, an SPSS Modeler stream deployed to the repository

3
About IBM SPSS Modeler

can be shared by multiple users, or accessed from the thin-client application IBM SPSS Modeler
Advantage. You install the adapter on the system that hosts the repository.

IBM SPSS Modeler Editions
SPSS Modeler is available in the following editions.
SPSS Modeler Professional

SPSS Modeler Professional provides all the tools you need to work with most types of structured
data, such as behaviors and interactions tracked in CRM systems, demographics, purchasing
behavior and sales data.
SPSS Modeler Premium

SPSS Modeler Premium is a separately-licensed product that extends SPSS Modeler Professional
to work with specialized data such as that used for entity analytics or social networking, and with
unstructured text data. SPSS Modeler Premium comprises the following components.
IBM® SPSS® Modeler Entity Analytics adds a completely new dimension to IBM® SPSS®
Modeler predictive analytics. Whereas predictive analytics attempts to predict future behavior
from past data, entity analytics focuses on improving the coherence and consistency of current
data by resolving identity conflicts within the records themselves. An identity can be that of an
individual, an organization, an object, or any other entity for which ambiguity might exist. Identity
resolution can be vital in a number of fields, including customer relationship management, fraud
detection, anti-money laundering, and national and international security.
IBM SPSS Modeler Social Network Analysis transforms information about relationships into
fields that characterize the social behavior of individuals and groups. Using data describing
the relationships underlying social networks, IBM® SPSS® Modeler Social Network Analysis
identifies social leaders who influence the behavior of others in the network. In addition, you can
determine which people are most affected by other network participants. By combining these
results with other measures, you can create comprehensive profiles of individuals on which to
base your predictive models. Models that include this social information will perform better than
models that do not.
IBM® SPSS® Modeler Text Analytics uses advanced linguistic technologies and Natural
Language Processing (NLP) to rapidly process a large variety of unstructured text data, extract
and organize the key concepts, and group these concepts into categories. Extracted concepts and
categories can be combined with existing structured data, such as demographics, and applied to
modeling using the full suite of SPSS Modeler data mining tools to yield better and more focused
decisions.

4
Chapter 1

IBM SPSS Modeler Documentation
Documentation in online help format is available from the Help menu of SPSS Modeler. This
includes documentation for SPSS Modeler, SPSS Modeler Server, and SPSS Modeler Solution
Publisher, as well as the Applications Guide and other supporting materials.
Complete documentation for each product (including installation instructions) is available in PDF
format under the \Documentation folder on each product DVD. Installation documents can also be
downloaded from the web at http://www-01.ibm.com/support/docview.wss?uid=swg27023172.
Documentation in both formats is also available from the SPSS Modeler Information Center at
http://publib.boulder.ibm.com/infocenter/spssmodl/v15r0m0/.

SPSS Modeler Professional Documentation
The SPSS Modeler Professional documentation suite (excluding installation instructions) is
as follows.

IBM SPSS Modeler User’s Guide. General introduction to using SPSS Modeler, including how
to build data streams, handle missing values, build CLEM expressions, work with projects and
reports, and package streams for deployment to IBM SPSS Collaboration and Deployment
Services, Predictive Applications, or IBM SPSS Modeler Advantage.

IBM SPSS Modeler Source, Process, and Output Nodes. Descriptions of all the nodes used to
read, process, and output data in different formats. Effectively this means all nodes other
than modeling nodes.

IBM SPSS Modeler Modeling Nodes. Descriptions of all the nodes used to create data mining
models. IBM® SPSS® Modeler offers a variety of modeling methods taken from machine
learning, artificial intelligence, and statistics.

IBM SPSS Modeler Algorithms Guide. Descriptions of the mathematical foundations of the
modeling methods used in SPSS Modeler. This guide is available in PDF format only.

IBM SPSS Modeler Applications Guide. The examples in this guide provide brief, targeted
introductions to specific modeling methods and techniques. An online version of this guide
is also available from the Help menu. For more information, see the topic Application
Examples on p. 5.

IBM SPSS Modeler Scripting and Automation. Information on automating the system through
scripting, including the properties that can be used to manipulate nodes and streams.

IBM SPSS Modeler Deployment Guide. Information on running SPSS Modeler streams and
scenarios as steps in processing jobs under IBM® SPSS® Collaboration and Deployment
Services Deployment Manager.

IBM SPSS Modeler CLEF Developer’s Guide. CLEF provides the ability to integrate third-party
programs such as data processing routines or modeling algorithms as nodes in SPSS Modeler.

IBM SPSS Modeler In-Database Mining Guide. Information on how to use the power of your
database to improve performance and extend the range of analytical capabilities through
third-party algorithms.

IBM SPSS Modeler Server Administration and Performance Guide. Information on how to
configure and administer IBM® SPSS® Modeler Server.

5
About IBM SPSS Modeler

IBM SPSS Modeler Administration Console User Guide. Information on installing and using the
console user interface for monitoring and configuring SPSS Modeler Server. The console is
implemented as a plug-in to the Deployment Manager application.

IBM SPSS Modeler Solution Publisher Guide. SPSS Modeler Solution Publisher is an add-on
component that enables organizations to publish streams for use outside of the standard
SPSS Modeler environment.

IBM SPSS Modeler CRISP-DM Guide. Step-by-step guide to using the CRISP-DM methodology
for data mining with SPSS Modeler.

IBM SPSS Modeler Batch User’s Guide. Complete guide to using IBM SPSS Modeler in batch
mode, including details of batch mode execution and command-line arguments. This guide
is available in PDF format only.

SPSS Modeler Premium Documentation
The SPSS Modeler Premium documentation suite (excluding installation instructions) is as
follows.

IBM SPSS Modeler Entity Analytics User Guide. Information on using entity analytics with
SPSS Modeler, covering repository installation and configuration, entity analytics nodes,
and administrative tasks.

IBM SPSS Modeler Social Network Analysis User Guide. A guide to performing social network
analysis with SPSS Modeler, including group analysis and diffusion analysis.

SPSS Modeler Text Analytics User’s Guide. Information on using text analytics with SPSS
Modeler, covering the text mining nodes, interactive workbench, templates, and other
resources.

IBM SPSS Modeler Text Analytics Administration Console User Guide. Information on installing
and using the console user interface for monitoring and configuring IBM® SPSS® Modeler
Server for use with SPSS Modeler Text Analytics . The console is implemented as a plug-in
to the Deployment Manager application.

Application Examples
While the data mining tools in SPSS Modeler can help solve a wide variety of business and
organizational problems, the application examples provide brief, targeted introductions to specific
modeling methods and techniques. The data sets used here are much smaller than the enormous
data stores managed by some data miners, but the concepts and methods involved should be
scalable to real-world applications.
You can access the examples by clicking Application Examples on the Help menu in SPSS
Modeler. The data files and sample streams are installed in the Demos folder under the product
installation directory. For more information, see the topic Demos Folder on p. 6.
Database modeling examples. See the examples in the IBM SPSS Modeler In-Database Mining
Guide.
Scripting examples. See the examples in the IBM SPSS Modeler Scripting and Automation Guide.

6
Chapter 1

Demos Folder
The data files and sample streams used with the application examples are installed in the Demos
folder under the product installation directory. This folder can also be accessed from the IBM
SPSS Modeler 15 program group on the Windows Start menu, or by clicking Demos on the list of
recent directories in the File Open dialog box.
Figure 1-1
Selecting the Demos folder from the list of recently-used directories

Chapter

New Features
New and Changed Features in IBM SPSS Modeler 15
From this release onwards, IBM® SPSS® Modeler has the following editions.

IBM® SPSS® Modeler Professional is the new name for the existing SPSS Modeler product.

IBM® SPSS® Modeler Premium is a separately-licensed product that provides additional
features to those supplied by SPSS Modeler Professional.

The new features for these editions are described in the following sections.

New features in IBM SPSS Modeler Professional
The IBM® SPSS® Modeler Professional edition adds the following features in this release.
GLMM modeling node. Generalized linear mixed models (GLMMs) extend the linear model so
that: the target is linearly related to the factors and covariates via a specified link function; the
target can have a non-normal distribution; and the observations can be correlated. Generalized
linear mixed models cover a wide variety of models, from simple linear regression to complex
multilevel models for non-normal longitudinal data. For more information, see the topic New
Nodes in This Release on p. 10.
Support for maps in the Graphboard node. The Graphboard node now includes support for a large
number of map types. These include choropleths (where regions can be given different colors
or patterns to indicate different values) and point overlay maps (where geospatial points are
overlaid on the map).

IBM® SPSS® Modeler ships with several map files, but you can use the Map Conversion Utility
to convert your existing map shapefiles for use with the Graphboard Template Chooser.
Netezza Time Series and Generalized Linear nodes. Two new nodes are available for IBM®
Netezza® Analytics in-database mining: Time Series and Generalized Linear. For more
information, see the topic New Nodes in This Release on p. 10.
Netezza nodes enabled through Helper Applications. The Netezza Analytics database modeling
nodes are now enabled in the same way as the other database modeling nodes.
Zooming in and out on the stream view. It is now possible to scale the entire stream view up or down
from the standard size. This feature is particularly useful for gaining an overall view of a complex
stream, or for minimizing the number of pages needed to print a stream. For more information,
see the topic Changing the icon size for a stream in Chapter 3 on p. 24.
© Copyright IBM Corporation 1994, 2012.

8
Chapter 2

Default settings for database connections. You can now specify default settings for SQL Server
and Oracle database connections, as well as those already supported for IBM DB2 InfoSphere
Warehouse.
Stream properties and optimization redesign. The Options tab on the Stream Properties dialog box
has been redesigned to group the options into categories. The Optimization options have also
moved from User Options to Stream Properties. For more information, see the topic Setting
Options for Streams in Chapter 5 on p. 54.
Node execution timing. You can now set an option to display individual execution times for
the nodes in a stream. For more information, see the topic Viewing Node Execution Times in
Chapter 5 on p. 67.

You can also set an option (time_ecode_execution_log) in the server configuration file to record
these execution times in the message log.
Stream parameters in SQL queries from Database source node. You can now include SPSS Modeler
stream parameters in SQL queries that you enter in the Database source node.
Expression Builder supports in-database functions. If a stream connects to a database through a
Database source node and you use the Expression Builder with a downstream node, you can
include in-database functions from the connected database directly in the expression you are
building. For more information, see the topic Selecting Functions in Chapter 7 on p. 120.
IBM Cognos BI node enhancements. The Cognos BI source node now supports importing Cognos
list reports as well as data, and additionally supports the use of parameters and filters.

For the Cognos BI source and export nodes, SPSS Modeler now automatically detects the version
of IBM Cognos BI in use.
Enhancements to Aggregate node. The Aggregate node now supports several new aggregation
modes for aggregate fields: median, count, variance, and first and third quartiles.
Merge node supports conditional merge. You can now perform input record merges that depend on
satisfying a condition. You can specify the condition directly in the node, or build the condition
using the Expression Builder.
Enhancements to in-database mining nodes for IBM DB2 InfoSphere Warehouse. For in-database
mining with IBM DB2 InfoSphere Warehouse, the ISW Clustering node now supports the
Enhanced BIRCH algorithm in addition to demographic and Kohonen clustering. In addition, the
ISW Association node provides a choice of layout for non-transactional (tabular) data.
Table compression for database export. When exporting to a database, you can now specify table
compression options for SQL Server and Oracle database connections, as well as those already
supported for IBM DB2 InfoSphere Warehouse.
Bulk loading for database export. Additional help information is available for database bulk loading
using an external loader program.

9
New Features

SQL generation enhancements. The Aggregate node now supports SQL generation for date, time,
timestamp, and string data types, in addition to integer and real. With IBM Netezza databases, the
Sample node supports SQL generation for simple and complex sampling, and the Binning node
supports SQL generation for all binning methods except Tiles.
In-database model scoring. For IBM DB2 for z/OS, IBM Netezza and Teradata databases, it is
possible to enable SQL pushback of many of the model nuggets to carry out model scoring (as
opposed to in-database mining) within the database. To do this, you can install a scoring adapter
into the database. When you publish a model for the scoring adapter, the model is enabled to use
the user-defined function (UDF) capabilities of the database to perform the scoring.

A new configuration option, db_udf_enabled in options.cfg, causes the SQL generation option
to generate UDF SQL by default.
New format for database connection in batch mode. The format for specifying a database connection
in batch mode has changed to a single argument, to be consistent with the way it is specified in
scripting.
Enhancements to SPSS Statistics integration. On the Statistics Output node, additional procedures
are available on the Syntax tab through the Select a dialog button. The Regression submenu
now supports Partial Least Squares regression, and there is a new Forecasting submenu with the
following options: Spectral Analysis, Sequence Charts, Autocorrelations, and Cross-correlations.
For more information, see the SPSS Statistics documentation.

The Syntax tab of the Statistics Output node also has a new option to generate a Statistics File
source node for importing the data that results from running a stream containing the node. This is
useful where a procedure writes fields such as scores to the active dataset in addition to displaying
output, as these fields would otherwise not be visible.
Non-root user on UNIX servers. If you have SPSS Modeler Server installed on a UNIX server, you
can now install, configure, and start and stop SPSS Modeler Server as a non-root user without the
need for a private password database.
Deployed streams can now access IBM SPSS Collaboration and Deployment Services model
management features. When a stream is deployed to IBM SPSS Collaboration and Deployment
Services as a stream, it can now use the same model management features as it could if deployed
as a scenario. These features include evaluation, refresh, score, and champion/challenger.
Improved method of changing ODBC connection for SPSS Modeler stream and scenario job steps. For
stream and scenario job steps in IBM SPSS Collaboration and Deployment Services, changes to
an ODBC connection and related logon credentials apply to all related job steps. This means that
you no longer have to change the job steps one by one.
Choice of execution branch in deployed streams. For stream job steps in IBM SPSS Collaboration
and Deployment Services, if the stream contains branches you can now choose one or more
stream branches to execute.

10
Chapter 2

New features in IBM SPSS Modeler Premium
IBM® SPSS® Modeler Premium is a separately-licensed product that provides additional features
to those supplied by IBM® SPSS® Modeler Professional. Previously, SPSS Modeler Premium
included only IBM® SPSS® Modeler Text Analytics . The full set of SPSS Modeler Premium
features is now as follows.

SPSS Modeler Text Analytics

IBM® SPSS® Modeler Entity Analytics

IBM® SPSS® Modeler Social Network Analysis

SPSS Modeler Text Analytics uses advanced linguistic technologies and Natural Language
Processing (NLP) to rapidly process a large variety of unstructured text data, extract and organize
the key concepts, and group these concepts into categories. Extracted concepts and categories
can be combined with existing structured data, such as demographics, and applied to modeling
using the full suite of IBM® SPSS® Modeler data mining tools to yield better and more focused
decisions.
IBM SPSS Modeler Entity Analytics adds a completely new dimension to SPSS Modeler
predictive analytics. Whereas predictive analytics attempts to predict future behavior from past
data, entity analytics focuses on improving the coherence and consistency of current data by
resolving identity conflicts within the records themselves. An identity can be that of an individual,
an organization, an object, or any other entity for which ambiguity might exist. Identity resolution
can be vital in a number of fields, including customer relationship management, fraud detection,
anti-money laundering, and national and international security.
IBM SPSS Modeler Social Network Analysis transforms information about relationships into
fields that characterize the social behavior of individuals and groups. Using data describing the
relationships underlying social networks, IBM SPSS Modeler Social Network Analysis identifies
social leaders who influence the behavior of others in the network. In addition, you can determine
which people are most affected by other network participants. By combining these results with
other measures, you can create comprehensive profiles of individuals on which to base your
predictive models. Models that include this social information will perform better than models
that do not.
Note: SPSS Modeler Professional must be installed before installing any of the SPSS Modeler
Premium features.

New Nodes in This Release
IBM SPSS Modeler Professional
A generalized linear mixed model (GLMM) extends the linear model so that the target
can have a non-normal distribution, is linearly related to the factors and covariates via
a specified link function, and so that the observations can be correlated. Generalized
linear mixed models cover a wide variety of models, from simple linear regression to
complex multilevel models for non-normal longitudinal data.

11
New Features

The Netezza Time Series node analyzes time series data and can predict future
behavior from past events.

The Netezza Generalized Linear model expands the linear regression model so that
the dependent variable is related to the predictor variables by means of a specified
link function. Moreover, the model allows for the dependent variable to have a
non-normal distribution.

IBM SPSS Modeler Premium
The EA Export node is a terminal node that reads entity data from a data source and
exports the data to a repository for the purpose of entity resolution.

The Entity Analytics(EA) source node reads the resolved entities from the repository
and passes this data to the stream for further processing, such as formatting into
a report.

The Streaming EA node compares new cases against the entity data in the repository.

The SNA Group Analysis node builds a model of a social network based on input
data about the social groupings within the network. This technique identifies links
between the group members, and analyzes the interactions within the groups to
produce key performance indicators (KPIs). The KPIs can be used for purposes such
as churn prediction, anomaly detection, or group leader identification.
The SNA Diffusion Analysis node models the flow of information from a group
member to their social environment. A group member is assigned an initial weighting,
which is propagated across the network as a gradually reducing figure. This process
continues until each member of the network has been assigned a weighting relative to
the original group member, according to the amount of information that has reached
them. The individual member scores are then derived directly from these weightings.
In this way, for example, a service provider could identify customers that are at a
higher risk of churn according to their relationship with a recent churner.

Chapter

IBM SPSS Modeler Overview

Getting Started
As a data mining application, IBM® SPSS® Modeler offers a strategic approach to finding useful
relationships in large data sets. In contrast to more traditional statistical methods, you do not
necessarily need to know what you are looking for when you start. You can explore your data,
fitting different models and investigating different relationships, until you find useful information.

Starting IBM SPSS Modeler
To start the application, click:
Start > [All] Programs > IBM SPSS Modeler15 > IBM SPSS Modeler15

The main window is displayed after a few seconds.
Figure 3-1
IBM SPSS Modeler main application window

13
IBM SPSS Modeler Overview

Launching from the Command Line
You can use the command line of your operating system to launch IBM® SPSS® Modeler
as follows:
E On a computer where IBM® SPSS® Modeler is installed, open a DOS, or command-prompt,

window.
E To launch the SPSS Modeler interface in interactive mode, type the modelerclient command

followed by the required arguments; for example:
modelerclient -stream report.str -execute

The available arguments (flags) allow you to connect to a server, load streams, run scripts, or
specify other parameters as needed.

Connecting to IBM SPSS Modeler Server
IBM® SPSS® Modeler can be run as a standalone application, or as a client connected to IBM®
SPSS® Modeler Server directly or to an SPSS Modeler Server or server cluster through the
Coordinator of Processes plug-in from IBM® SPSS® Collaboration and Deployment Services.
The current connection status is displayed at the bottom left of the SPSS Modeler window.
Whenever you want to connect to a server, you can manually enter the server name to which
you want to connect or select a name that you have previously defined. However, if you have IBM
SPSS Collaboration and Deployment Services, you can search through a list of servers or server
clusters from the Server Login dialog box. The ability to browse through the Statistics services
running on a network is made available through the Coordinator of Processes.
Figure 3-2
Server Login dialog box

14
Chapter 3

To Connect to a Server
E On the Tools menu, click Server Login. The Server Login dialog box opens. Alternatively,

double-click the connection status area of the SPSS Modeler window.
E Using the dialog box, specify options to connect to the local server computer or select a connection

from the table.

Click Add or Edit to add or edit a connection. For more information, see the topic Adding and
Editing the IBM SPSS Modeler Server Connection on p. 14.

Click Search to access a server or server cluster in the Coordinator of Processes. For more
information, see the topic Searching for Servers in IBM SPSS Collaboration and Deployment
Services on p. 16.

Server table. This table contains the set of defined server connections. The table displays the
default connection, server name, description, and port number. You can manually add a new
connection, as well as select or search for an existing connection. To set a particular server as the
default connection, select the check box in the Default column in the table for the connection.
Default data path. Specify a path used for data on the server computer. Click the ellipsis button (...)
to browse to the required location.
Set Credentials. Leave this box unchecked to enable the single sign-on feature, which attempts
to log you in to the server using your local computer username and password details. If single
sign-on is not possible, or if you check this box to disable single sign-on (for example, to log in to
an administrator account), the following fields are enabled for you to enter your credentials.
User ID. Enter the user name with which to log on to the server.
Password. Enter the password associated with the specified user name.
Domain. Specify the domain used to log on to the server. A domain name is required only when
the server computer is in a different Windows domain than the client computer.
E Click OK to complete the connection.

To Disconnect from a Server
E On the Tools menu, click Server Login. The Server Login dialog box opens. Alternatively,

double-click the connection status area of the SPSS Modeler window.
E In the dialog box, select the Local Server and click OK.

Adding and Editing the IBM SPSS Modeler Server Connection
You can manually edit or add a server connection in the Server Login dialog box. By clicking
Add, you can access an empty Add/Edit Server dialog box in which you can enter server
connection details. By selecting an existing connection and clicking Edit in the Server Login
dialog box, the Add/Edit Server dialog box opens with the details for that connection so that
you can make any changes.

15
IBM SPSS Modeler Overview

Note: You cannot edit a server connection that was added from IBM® SPSS® Collaboration
and Deployment Services, since the name, port, and other details are defined in IBM SPSS
Collaboration and Deployment Services.
Figure 3-3
Server Login Add/Edit Server dialog box

To Add Server Connections
E On the Tools menu, click Server Login. The Server Login dialog box opens.
E In this dialog box, click Add. The Server Login Add/Edit Server dialog box opens.
E Enter the server connection details and click OK to save the connection and return to the Server

Server. Specify an available server or select one from the list. The server computer can be
identified by an alphanumeric name (for example, myserver) or an IP address assigned to the
server computer (for example, 202.123.456.78).

Port. Give the port number on which the server is listening. If the default does not work, ask
your system administrator for the correct port number.

Description. Enter an optional description for this server connection.

Ensure secure connection (use SSL). Specifies whether an SSL (Secure Sockets Layer)
connection should be used. SSL is a commonly used protocol for securing data sent over a
network. To use this feature, SSL must be enabled on the server hosting IBM® SPSS®
Modeler Server. If necessary, contact your local administrator for details.

To Edit Server Connections
E On the Tools menu, click Server Login. The Server Login dialog box opens.
E In this dialog box, select the connection you want to edit and then click Edit. The Server Login

Add/Edit Server dialog box opens.
E Change the server connection details and click OK to save the changes and return to the Server

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Chapter 3

Searching for Servers in IBM SPSS Collaboration and Deployment Services
Instead of entering a server connection manually, you can select a server or server cluster available
on the network through the Coordinator of Processes, available in IBM® SPSS® Collaboration
and Deployment Services. A server cluster is a group of servers from which the Coordinator of
Processes determines the server best suited to respond to a processing request.
Although you can manually add servers in the Server Login dialog box, searching for available
servers lets you connect to servers without requiring that you know the correct server name and
port number. This information is automatically provided. However, you still need the correct
logon information, such as username, domain, and password.
Note: If you do not have access to the Coordinator of Processes capability, you can still manually
enter the server name to which you want to connect or select a name that you have previously
defined. For more information, see the topic Adding and Editing the IBM SPSS Modeler Server
Connection on p. 14.
Figure 3-4
Search for Servers dialog box

To search for servers and clusters
E On the Tools menu, click Server Login. The Server Login dialog box opens.
E In this dialog box, click Search to open the Search for Servers dialog box. If you are not

logged on to IBM SPSS Collaboration and Deployment Services when you attempt to browse
the Coordinator of Processes, you will be prompted to do so. For more information, see the
topic Connecting to the Repository in Chapter 9 on p. 161.
E Select the server or server cluster from the list.
E Click OK to close the dialog box and add this connection to the table in the Server Login dialog box.

Changing the Temp Directory
Some operations performed by IBM® SPSS® Modeler Server may require temporary files to be
created. By default, IBM® SPSS® Modeler uses the system temporary directory to create temp
files. You can alter the location of the temporary directory using the following steps.
E Create a new directory called spss and subdirectory called servertemp.

17
IBM SPSS Modeler Overview
E Edit options.cfg, located in the /config directory of your SPSS Modeler installation directory. Edit

the temp_directory parameter in this file to read: temp_directory, "C:/spss/servertemp".
E After doing this, you must restart the SPSS Modeler Server service. You can do this by clicking
the Services tab on your Windows Control Panel. Just stop the service and then start it to activate

the changes you made. Restarting the machine will also restart the service.
All temp files will now be written to this new directory.
Note: The most common error when you are attempting to do this is to use the wrong type of
slashes. Because of SPSS Modeler’s UNIX history, forward slashes are used.

Starting Multiple IBM SPSS Modeler Sessions
If you need to launch more than one IBM® SPSS® Modeler session at a time, you must make
some changes to your IBM® SPSS® Modeler and Windows settings. For example, you may
need to do this if you have two separate server licenses and want to run two streams against two
different servers from the same client machine.
To enable multiple SPSS Modeler sessions:
E Click:
Start > [All] Programs > IBM SPSS Modeler15
E On the IBM SPSS Modeler15 shortcut (the one with the icon), right-click and select Properties.
E In the Target text box, add -noshare to the end of the string.
E In Windows Explorer, select:
Tools > Folder Options...
E On the File Types tab, select the SPSS Modeler Stream option and click Advanced.
E In the Edit File Type dialog box, select Open with SPSS Modeler and click Edit.
E In the Application used to perform action text box, add -noshare before the -stream argument.

IBM SPSS Modeler Interface at a Glance
At each point in the data mining process, IBM® SPSS® Modeler’s easy-to-use interface invites
your specific business expertise. Modeling algorithms, such as prediction, classification,
segmentation, and association detection, ensure powerful and accurate models. Model results
can easily be deployed and read into databases, IBM® SPSS® Statistics, and a wide variety
of other applications.
Working with SPSS Modeler is a three-step process of working with data.

First, you read data into SPSS Modeler.

Next, you run the data through a series of manipulations.

Finally, you send the data to a destination.

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Chapter 3

This sequence of operations is known as a data stream because the data flows record by record
from the source through each manipulation and, finally, to the destination—either a model or
type of data output.
Figure 3-5
A simple stream

IBM SPSS Modeler Stream Canvas
The stream canvas is the largest area of the IBM® SPSS® Modeler window and is where you will
build and manipulate data streams.
Streams are created by drawing diagrams of data operations relevant to your business on the
main canvas in the interface. Each operation is represented by an icon or node, and the nodes are
linked together in a stream representing the flow of data through each operation.
You can work with multiple streams at one time in SPSS Modeler, either in the same stream
canvas or by opening a new stream canvas. During a session, streams are stored in the Streams
manager, at the upper right of the SPSS Modeler window.

Nodes Palette
Most of the data and modeling tools in IBM® SPSS® Modeler reside in the Nodes Palette, across
the bottom of the window below the stream canvas.
For example, the Record Ops palette tab contains nodes that you can use to perform operations
on the data records, such as selecting, merging, and appending.
To add nodes to the canvas, double-click icons from the Nodes Palette or drag and drop them
onto the canvas. You then connect them to create a stream, representing the flow of data.
Figure 3-6
Record Ops tab on the nodes palette

Each palette tab contains a collection of related nodes used for different phases of stream
operations, such as:

Sources. Nodes bring data into SPSS Modeler.

Record Ops. Nodes perform operations on data records, such as selecting, merging, and
appending.

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IBM SPSS Modeler Overview

Field Ops. Nodes perform operations on data fields, such as filtering, deriving new fields, and
determining the measurement level for given fields.

Graphs. Nodes graphically display data before and after modeling. Graphs include plots,
histograms, web nodes, and evaluation charts.

Modeling. Nodes use the modeling algorithms available in SPSS Modeler, such as neural nets,
decision trees, clustering algorithms, and data sequencing.

Database Modeling. Nodes use the modeling algorithms available in Microsoft SQL Server,
IBM DB2, and Oracle databases.

Output. Nodes produce a variety of output for data, charts, and model results that can be
viewed in SPSS Modeler.

Export. Nodes produce a variety of output that can be viewed in external applications, such
as IBM® SPSS® Data Collection or Excel.

SPSS Statistics. Nodes import data from, or export data to, IBM® SPSS® Statistics, as well as
running SPSS Statistics procedures.

As you become more familiar with SPSS Modeler, you can customize the palette contents for
your own use. For more information, see the topic Customizing the Nodes Palette in Chapter 12
on p. 223.
Located below the Nodes Palette, a report pane provides feedback on the progress of various
operations, such as when data is being read into the data stream. Also located below the Nodes
Palette, a status pane provides information on what the application is currently doing, as well as
indications of when user feedback is required.

IBM SPSS Modeler Managers
At the top right of the window is the managers pane. This has three tabs, which are used to
manage streams, output and models.
You can use the Streams tab to open, rename, save, and delete the streams created in a session.
Figure 3-7
Streams tab

The Outputs tab contains a variety of files, such as graphs and tables, produced by stream
operations in IBM® SPSS® Modeler. You can display, save, rename, and close the tables, graphs,
and reports listed on this tab.

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Chapter 3
Figure 3-8
Outputs tab

The Models tab is the most powerful of the manager tabs. This tab contains all model nuggets,
which contain the models generated in SPSS Modeler, for the current session. These models can
be browsed directly from the Models tab or added to the stream in the canvas.
Figure 3-9
Models tab containing model nuggets

IBM SPSS Modeler Projects
On the lower right side of the window is the project pane, used to create and manage data mining
projects (groups of files related to a data mining task). There are two ways to view projects you
create in IBM® SPSS® Modeler—in the Classes view and the CRISP-DM view.
The CRISP-DM tab provides a way to organize projects according to the Cross-Industry
Standard Process for Data Mining, an industry-proven, nonproprietary methodology. For both
experienced and first-time data miners, using the CRISP-DM tool will help you to better organize
and communicate your efforts.

21
IBM SPSS Modeler Overview
Figure 3-10
CRISP-DM view

The Classes tab provides a way to organize your work in SPSS Modeler categorically—by the
types of objects you create. This view is useful when taking inventory of data, streams, and
models.
Figure 3-11
Classes view

IBM SPSS Modeler Toolbar
At the top of the IBM® SPSS® Modeler window, you will find a toolbar of icons that provides a
number of useful functions. Following are the toolbar buttons and their functions.

Create new stream

Open stream

Save stream

Print current stream

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Chapter 3

Cut & move to clipboard

Copy to clipboard

Paste selection

Undo last action

Redo

Search for nodes

Edit stream properties

Preview SQL generation

Run current stream

Run stream selection

Stop stream (Active only while
stream is running)

Add SuperNode

Zoom in (SuperNodes only)

Zoom out (SuperNodes only)

No markup in stream

Insert comment

Hide stream markup (if any)

Show hidden stream markup

Open stream in IBM® SPSS®
Modeler Advantage

Stream markup consists of stream comments, model links, and scoring branch indications.
For more information on stream comments, see Adding Comments and Annotations to Nodes
and Streams on p. 78.
For more information on scoring branch indications, see The Scoring Branch on p. 188.
Model links are described in the IBM SPSS Modeling Nodes guide.

23
IBM SPSS Modeler Overview

Customizing the Toolbar
You can change various aspects of the toolbar, such as:

Whether it is displayed

Whether the icons have tooltips available

Whether it uses large or small icons

To turn the toolbar display on and off:
E On the main menu, click:
View > Toolbar > Display

To change the tooltip or icon size settings:
E On the main menu, click:
View > Toolbar > Customize

Click Show ToolTips or Large Buttons as required.

Customizing the IBM SPSS Modeler Window
Using the dividers between various portions of the IBM® SPSS® Modeler interface, you can
resize or close tools to meet your preferences. For example, if you are working with a large
stream, you can use the small arrows located on each divider to close the nodes palette, managers
pane, and project pane. This maximizes the stream canvas, providing enough work space for
large or multiple streams.
Alternatively, on the View menu, click Nodes Palette, Managers, or Project to turn the display of
these items on or off.

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Chapter 3
Figure 3-12
Maximized stream canvas

As an alternative to closing the nodes palette, and the managers and project panes, you can use the
stream canvas as a scrollable page by moving vertically and horizontally with the scrollbars at the
side and bottom of the SPSS Modeler window.
You can also control the display of screen markup, which consists of stream comments, model
links, and scoring branch indications. To turn this display on or off, click:
View > Stream Markup

Changing the icon size for a stream
You can change the size of the stream icons in the following ways.

Through a stream property setting

Through a pop-up menu in the stream

Using the keyboard

You can scale the entire stream view to one of a number of sizes between 8% and 200% of the
standard icon size.

25
IBM SPSS Modeler Overview
Figure 3-13
Changing the icon size

To scale the entire stream (stream properties method)
E From the main menu, choose
Tools > Stream Properties > Options > Layout.
E Choose the size you want from the Icon Size menu.
E Click Apply to see the result.
E Click OK to save the change.

To scale the entire stream (menu method)
E Right-click the stream background on the canvas.
E Choose Icon Size and select the size you want.

To scale the entire stream (keyboard method)
E Press Ctrl + [-] on the main keyboard to zoom out to the next smaller size.
E Press Ctrl + Shift + [+] on the main keyboard to zoom in to the next larger size.

This feature is particularly useful for gaining an overall view of a complex stream. You can also
use it to minimize the number of pages needed to print a stream.

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Chapter 3

Using the Mouse in IBM SPSS Modeler
The most common uses of the mouse in IBM® SPSS® Modeler include the following:

Single-click. Use either the right or left mouse button to select options from menus, open
pop-up menus, and access various other standard controls and options. Click and hold the
button to move and drag nodes.

Double-click. Double-click using the left mouse button to place nodes on the stream canvas
and edit existing nodes.

Middle-click. Click the middle mouse button and drag the cursor to connect nodes on the
stream canvas. Double-click the middle mouse button to disconnect a node. If you do not
have a three-button mouse, you can simulate this feature by pressing the Alt key while
clicking and dragging the mouse.

Using Shortcut Keys
Many visual programming operations in IBM® SPSS® Modeler have shortcut keys associated
with them. For example, you can delete a node by clicking the node and pressing the Delete key
on your keyboard. Likewise, you can quickly save a stream by pressing the S key while holding
down the Ctrl key. Control commands like this one are indicated by a combination of Ctrl and
another key—for example, Ctrl+S.
There are a number of shortcut keys used in standard Windows operations, such as Ctrl+X to
cut. These shortcuts are supported in SPSS Modeler along with the following application-specific
shortcuts.
Note: In some cases, old shortcut keys used in SPSS Modeler conflict with standard Windows
shortcut keys. These old shortcuts are supported with the addition of the Alt key. For example,
Ctrl+Alt+C can be used to toggle the cache on and off.
Table 3-1
Supported shortcut keys

Shortcut
Key
Ctrl+A
Ctrl+X
Ctrl+N
Ctrl+O
Ctrl+P
Ctrl+C
Ctrl+V
Ctrl+Z
Ctrl+Q
Ctrl+W
Ctrl+E
Ctrl+S
Alt+Arrow
keys
Shift+F10

Function
Select all
Cut
New stream
Open stream
Print
Copy
Paste
Undo
Select all nodes downstream of the selected node
Deselect all downstream nodes (toggles with Ctrl+Q)
Run from selected node
Save current stream
Move selected nodes on the stream canvas in the direction
of the arrow used
Open the pop-up menu for the selected node

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IBM SPSS Modeler Overview
Table 3-2
Supported shortcuts for old hot keys

Shortcut
Key
Ctrl+Alt+D
Ctrl+Alt+L
Ctrl+Alt+R
Ctrl+Alt+U
Ctrl+Alt+C
Ctrl+Alt+F
Ctrl+Alt+X
Ctrl+Alt+Z
Delete

Function
Duplicate node
Load node
Rename node
Create User Input node
Toggle cache on/off
Flush cache
Expand SuperNode
Zoom in/zoom out
Delete node or connection

Printing
The following objects can be printed in IBM® SPSS® Modeler:

Stream diagrams

Graphs

Tables

Reports (from the Report node and Project Reports)

Scripts (from the stream properties, Standalone Script, or SuperNode script dialog boxes)

Models (Model browsers, dialog box tabs with current focus, tree viewers)

Annotations (using the Annotations tab for output)

To print an object:

To print without previewing, click the Print button on the toolbar.

To set up the page before printing, select Page Setup from the File menu.

To preview before printing, select Print Preview from the File menu.

To view the standard print dialog box with options for selecting printers, and specifying
appearance options, select Print from the File menu.

Automating IBM SPSS Modeler
Since advanced data mining can be a complex and sometimes lengthy process, IBM® SPSS®
Modeler includes several types of coding and automation support.

Control Language for Expression Manipulation (CLEM) is a language for analyzing
and manipulating the data that flows along SPSS Modeler streams. Data miners use CLEM
extensively in stream operations to perform tasks as simple as deriving profit from cost and

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Chapter 3

revenue data or as complex as transforming web log data into a set of fields and records with
usable information. For more information, see the topic About CLEM in Chapter 7 on p. 105.

Scripting is a powerful tool for automating processes in the user interface. Scripts can
perform the same kinds of actions that users perform with a mouse or a keyboard. You can
set options for nodes and perform derivations using a subset of CLEM. You can also specify
output and manipulate generated models.

Chapter

Understanding Data Mining

Data Mining Overview
Through a variety of techniques, data mining identifies nuggets of information in bodies of data.
Data mining extracts information in such a way that it can be used in areas such as decision
support, prediction, forecasts, and estimation. Data is often voluminous but of low value and with
little direct usefulness in its raw form. It is the hidden information in the data that has value.
In data mining, success comes from combining your (or your expert’s) knowledge of the
data with advanced, active analysis techniques in which the computer identifies the underlying
relationships and features in the data. The process of data mining generates models from historical
data that are later used for predictions, pattern detection, and more. The technique for building
these models is called machine learning or modeling.
Modeling Techniques

IBM® SPSS® Modeler includes a number of machine-learning and modeling technologies, which
can be roughly grouped according to the types of problems they are intended to solve.

Predictive modeling methods include decision trees, neural networks, and statistical models.

Clustering models focus on identifying groups of similar records and labeling the records
according to the group to which they belong. Clustering methods include Kohonen, k-means,
and TwoStep.

Association rules associate a particular conclusion (such as the purchase of a particular
product) with a set of conditions (the purchase of several other products).

Screening models can be used to screen data to locate fields and records that are most likely to
be of interest in modeling and identify outliers that may not fit known patterns. Available
methods include feature selection and anomaly detection.

Data Manipulation and Discovery

SPSS Modeler also includes many facilities that let you apply your expertise to the data:

Data manipulation. Constructs new data items derived from existing ones and breaks down the
data into meaningful subsets. Data from a variety of sources can be merged and filtered.

Browsing and visualization. Displays aspects of the data using the Data Audit node to perform
an initial audit including graphs and statistics. Advanced visualization includes interactive
graphics, which can be exported for inclusion in project reports.

Statistics. Confirms suspected relationships between variables in the data. Statistics from
IBM® SPSS® Statistics can also be used within SPSS Modeler.

Hypothesis testing. Constructs models of how the data behaves and verifies these models.

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Chapter 4

Typically, you will use these facilities to identify a promising set of attributes in the data. These
attributes can then be fed to the modeling techniques, which will attempt to identify underlying
rules and relationships.
Typical Applications

Typical applications of data mining techniques include the following:
Direct mail. Determine which demographic groups have the highest response rate. Use this
information to maximize the response to future mailings.
Credit scoring. Use an individual’s credit history to make credit decisions.
Human resources. Understand past hiring practices and create decision rules to streamline the
hiring process.
Medical research. Create decision rules that suggest appropriate procedures based on medical
evidence.
Market analysis. Determine which variables, such as geography, price, and customer
characteristics, are associated with sales.
Quality control. Analyze data from product manufacturing and identify variables determining
product defects.
Policy studies. Use survey data to formulate policy by applying decision rules to select the most
important variables.
Health care. User surveys and clinical data can be combined to discover variables that contribute
to health.
Terminology

The terms attribute, field, and variable refer to a single data item common to all cases under
consideration. A collection of attribute values that refers to a specific case is called a record, an
example, or a case.

Assessing the Data
Data mining is not likely to be fruitful unless the data you want to use meets certain criteria. The
following sections present some of the aspects of the data and its application that you should
consider.
Ensure that the data is available

This may seem obvious, but be aware that although data might be available, it may not be in a
form that can be used easily. IBM® SPSS® Modeler can import data from databases (through
ODBC) or from files. The data, however, might be held in some other form on a machine that
cannot be directly accessed. It will need to be downloaded or dumped in a suitable form before it
can be used. It might be scattered among different databases and sources and need to be pulled

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Understanding Data Mining

together. It may not even be online. If it exists only on paper, data entry will be required before
you can begin data mining.
Check whether the data covers the relevant attributes

The object of data mining is to identify relevant attributes, so including this check may seem odd
at first. It is very useful, however, to look at what data is available and to try to identify the likely
relevant factors that are not recorded. In trying to predict ice cream sales, for example, you may
have a lot of information about retail outlets or sales history, but you may not have weather
and temperature information, which is likely to play a significant role. Missing attributes do
not necessarily mean that data mining will not produce useful results, but they can limit the
accuracy of resulting predictions.
A quick way of assessing the situation is to perform a comprehensive audit of your data.
Before moving on, consider attaching a Data Audit node to your data source and running it to
generate a full report.
Beware of noisy data

Data often contains errors or may contain subjective, and therefore variable, judgments. These
phenomena are collectively referred to as noise. Sometimes noise in data is normal. There may
well be underlying rules, but they may not hold for 100% of the cases.
Typically, the more noise there is in data, the more difficult it is to get accurate results.
However, SPSS Modeler’s machine-learning methods are able to handle noisy data and have been
used successfully on data sets containing almost 50% noise.
Ensure that there is sufficient data

In data mining, it is not necessarily the size of a data set that is important. The representativeness
of the data set is far more significant, together with its coverage of possible outcomes and
combinations of variables.
Typically, the more attributes that are considered, the more records that will be needed to
give representative coverage.
If the data is representative and there are general underlying rules, it may well be that a data
sample of a few thousand (or even a few hundred) records will give equally good results as a
million—and you will get the results more quickly.
Seek out the experts on the data

In many cases, you will be working on your own data and will therefore be highly familiar with
its content and meaning. However, if you are working on data for another department of your
organization or for a client, it is highly desirable that you have access to experts who know the
data. They can guide you in the identification of relevant attributes and can help to interpret the
results of data mining, distinguishing the true nuggets of information from “fool’s gold,” or
artifacts caused by anomalies in the data sets.

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Chapter 4

A Strategy for Data Mining
As with most business endeavors, data mining is much more effective if done in a planned,
systematic way. Even with cutting-edge data mining tools, such as IBM® SPSS® Modeler, the
majority of the work in data mining requires a knowledgeable business analyst to keep the process
on track. To guide your planning, answer the following questions:

What substantive problem do you want to solve?

What data sources are available, and what parts of the data are relevant to the current problem?

What kind of preprocessing and data cleaning do you need to do before you start mining
the data?

What data mining technique(s) will you use?

How will you evaluate the results of the data mining analysis?

How will you get the most out of the information you obtained from data mining?

The typical data mining process can become complicated very quickly. There is a lot to keep track
of—complex business problems, multiple data sources, varying data quality across data sources,
an array of data mining techniques, different ways of measuring data mining success, and so on.
To stay on track, it helps to have an explicitly defined process model for data mining. The
process model helps you answer the questions listed earlier in this section, and makes sure the
important points are addressed. It serves as a data mining road map so that you will not lose your
way as you dig into the complexities of your data.
The data mining process suggested for use with SPSS Modeler is the Cross-Industry Standard
Process for Data Mining (CRISP-DM). As you can tell from the name, this model is designed as a
general model that can be applied to a wide variety of industries and business problems.

The CRISP-DM Process Model
The general CRISP-DM process model includes six phases that address the main issues in data
mining. The six phases fit together in a cyclical process designed to incorporate data mining
into your larger business practices.

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Understanding Data Mining
Figure 4-1
CRISP-DM process model

The six phases include:

Business understanding. This is perhaps the most important phase of data mining. Business
understanding includes determining business objectives, assessing the situation, determining
data mining goals, and producing a project plan.

Data understanding. Data provides the “raw materials” of data mining. This phase addresses
the need to understand what your data resources are and the characteristics of those resources.
It includes collecting initial data, describing data, exploring data, and verifying data quality.
The Data Audit node available from the Output nodes palette is an indispensable tool for
data understanding.

Data preparation. After cataloging your data resources, you will need to prepare your data for
mining. Preparations include selecting, cleaning, constructing, integrating, and formatting
data.

Modeling. This is, of course, the flashy part of data mining, where sophisticated analysis
methods are used to extract information from the data. This phase involves selecting modeling
techniques, generating test designs, and building and assessing models.

Evaluation. Once you have chosen your models, you are ready to evaluate how the data mining
results can help you to achieve your business objectives. Elements of this phase include
evaluating results, reviewing the data mining process, and determining the next steps.

Deployment. Now that you have invested all of this effort, it is time to reap the benefits. This
phase focuses on integrating your new knowledge into your everyday business processes to
solve your original business problem. This phase includes plan deployment, monitoring and
maintenance, producing a final report, and reviewing the project.

There are some key points in this process model. First, while there is a general tendency for the
process to flow through the steps in the order outlined in the previous paragraphs, there are also a
number of places where the phases influence each other in a nonlinear way. For example, data
preparation usually precedes modeling. However, decisions made and information gathered
during the modeling phase can often lead you to rethink parts of the data preparation phase, which
can then present new modeling issues. The two phases feed back on each other until both phases

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Chapter 4

have been resolved adequately. Similarly, the evaluation phase can lead you to reevaluate your
original business understanding, and you may decide that you have been trying to answer the
wrong question. At this point, you can revise your business understanding and proceed through
the rest of the process again with a better target in mind.
The second key point is the iterative nature of data mining. You will rarely, if ever, simply
plan a data mining project, complete it, and then pack up your data and go home. Data mining to
address your customers’ demands is an ongoing endeavor. The knowledge gained from one cycle
of data mining will almost invariably lead to new questions, new issues, and new opportunities
to identify and meet your customers’ needs. Those new questions, issues, and opportunities can
usually be addressed by mining your data once again. This process of mining and identifying new
opportunities should become part of the way you think about your business and a cornerstone of
your overall business strategy.
This introduction provides only a brief overview of the CRISP-DM process model. For
complete details on the model, consult the following resources:

The CRISP-DM Guide, which can be accessed along with other documentation from the
\Documentation folder on the installation disk.

The CRISP-DM Help system, available from the Start menu or by clicking CRISP-DM Help on
the Help menu in IBM® SPSS® Modeler.

Types of Models
IBM® SPSS® Modeler offers a variety of modeling methods taken from machine learning,
artificial intelligence, and statistics. The methods available on the Modeling palette allow you
to derive new information from your data and to develop predictive models. Each method has
certain strengths and is best suited for particular types of problems.
The SPSS Modeler Applications Guide provides examples for many of these methods, along
with a general introduction to the modeling process. This guide is available as an online tutorial,
and also in PDF format. For more information, see the topic Application Examples in Chapter 1
on p. 5.
Modeling methods are divided into three categories:

Classification

Association

Segmentation

Classification Models

Classification models use the values of one or more input fields to predict the value of one or
more output, or target, fields. Some examples of these techniques are: decision trees (C&R Tree,
QUEST, CHAID and C5.0 algorithms), regression (linear, logistic, generalized linear, and Cox
regression algorithms), neural networks, support vector machines, and Bayesian networks.
Classification models helps organizations to predict a known result, such as whether a customer
will buy or leave or whether a transaction fits a known pattern of fraud. Modeling techniques
include machine learning, rule induction, subgroup identification, statistical methods, and multiple
model generation.

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Understanding Data Mining

Classification nodes
The Auto Classifier node creates and compares a number of different models for
binary outcomes (yes or no, churn or do not churn, and so on), allowing you to
choose the best approach for a given analysis. A number of modeling algorithms are
supported, making it possible to select the methods you want to use, the specific
options for each, and the criteria for comparing the results. The node generates a set
of models based on the specified options and ranks the best candidates according to
the criteria you specify.
The Auto Numeric node estimates and compares models for continuous numeric
range outcomes using a number of different methods. The node works in the same
manner as the Auto Classifier node, allowing you to choose the algorithms to use
and to experiment with multiple combinations of options in a single modeling pass.
Supported algorithms include neural networks, C&R Tree, CHAID, linear regression,
generalized linear regression, and support vector machines (SVM). Models can be
compared based on correlation, relative error, or number of variables used.
The Classification and Regression (C&R) Tree node generates a decision tree that
allows you to predict or classify future observations. The method uses recursive
partitioning to split the training records into segments by minimizing the impurity
at each step, where a node in the tree is considered “pure” if 100% of cases in the
node fall into a specific category of the target field. Target and input fields can
be numeric ranges or categorical (nominal, ordinal, or flags); all splits are binary
(only two subgroups).
The QUEST node provides a binary classification method for building decision trees,
designed to reduce the processing time required for large C&R Tree analyses while
also reducing the tendency found in classification tree methods to favor inputs that
allow more splits. Input fields can be numeric ranges (continuous), but the target field
must be categorical. All splits are binary.
The CHAID node generates decision trees using chi-square statistics to identify
optimal splits. Unlike the C&R Tree and QUEST nodes, CHAID can generate
nonbinary trees, meaning that some splits have more than two branches. Target and
input fields can be numeric range (continuous) or categorical. Exhaustive CHAID is
a modification of CHAID that does a more thorough job of examining all possible
splits but takes longer to compute.
The C5.0 node builds either a decision tree or a rule set. The model works by splitting
the sample based on the field that provides the maximum information gain at each
level. The target field must be categorical. Multiple splits into more than two
subgroups are allowed.
The Decision List node identifies subgroups, or segments, that show a higher or lower
likelihood of a given binary outcome relative to the overall population. For example,
you might look for customers who are unlikely to churn or are most likely to respond
favorably to a campaign. You can incorporate your business knowledge into the
model by adding your own custom segments and previewing alternative models side
by side to compare the results. Decision List models consist of a list of rules in which
each rule has a condition and an outcome. Rules are applied in order, and the first rule
that matches determines the outcome.
Linear regression models predict a continuous target based on linear relationships
between the target and one or more predictors.

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The PCA/Factor node provides powerful data-reduction techniques to reduce
the complexity of your data. Principal components analysis (PCA) finds linear
combinations of the input fields that do the best job of capturing the variance in the
entire set of fields, where the components are orthogonal (perpendicular) to each
other. Factor analysis attempts to identify underlying factors that explain the pattern
of correlations within a set of observed fields. For both approaches, the goal is to
find a small number of derived fields that effectively summarizes the information in
the original set of fields.
The Feature Selection node screens input fields for removal based on a set of criteria
(such as the percentage of missing values); it then ranks the importance of remaining
inputs relative to a specified target. For example, given a data set with hundreds of
potential inputs, which are most likely to be useful in modeling patient outcomes?
Discriminant analysis makes more stringent assumptions than logistic regression but
can be a valuable alternative or supplement to a logistic regression analysis when
those assumptions are met.

Logistic regression is a statistical technique for classifying records based on values
of input fields. It is analogous to linear regression but takes a categorical target field
instead of a numeric range.

The Generalized Linear model expands the general linear model so that the
dependent variable is linearly related to the factors and covariates through a specified
link function. Moreover, the model allows for the dependent variable to have a
non-normal distribution. It covers the functionality of a wide number of statistical
models, including linear regression, logistic regression, loglinear models for count
data, and interval-censored survival models.
A generalized linear mixed model (GLMM) extends the linear model so that the target
can have a non-normal distribution, is linearly related to the factors and covariates via
a specified link function, and so that the observations can be correlated. Generalized
linear mixed models cover a wide variety of models, from simple linear regression to
complex multilevel models for non-normal longitudinal data.
The Cox regression node enables you to build a survival model for time-to-event data
in the presence of censored records. The model produces a survival function that
predicts the probability that the event of interest has occurred at a given time (t)
for given values of the input variables.
The Support Vector Machine (SVM) node enables you to classify data into one of
two groups without overfitting. SVM works well with wide data sets, such as those
with a very large number of input fields.

The Bayesian Network node enables you to build a probability model by combining
observed and recorded evidence with real-world knowledge to establish the likelihood
of occurrences. The node focuses on Tree Augmented Naïve Bayes (TAN) and
Markov Blanket networks that are primarily used for classification.

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Understanding Data Mining

The Self-Learning Response Model (SLRM) node enables you to build a model in
which a single new case, or small number of new cases, can be used to reestimate the
model without having to retrain the model using all data.

The Time Series node estimates exponential smoothing, univariate Autoregressive
Integrated Moving Average (ARIMA), and multivariate ARIMA (or transfer function)
models for time series data and produces forecasts of future performance. A Time
Series node must always be preceded by a Time Intervals node.
The k-Nearest Neighbor (KNN) node associates a new case with the category or value
of the k objects nearest to it in the predictor space, where k is an integer. Similar cases
are near each other and dissimilar cases are distant from each other.

Association Models

Association models find patterns in your data where one or more entities (such as events,
purchases, or attributes) are associated with one or more other entities. The models construct rule
sets that define these relationships. Here the fields within the data can act as both inputs and
targets. You could find these associations manually, but association rule algorithms do so much
more quickly, and can explore more complex patterns. Apriori and Carma models are examples of
the use of such algorithms. One other type of association model is a sequence detection model,
which finds sequential patterns in time-structured data.
Association models are most useful when predicting multiple outcomes—for example, customers
who bought product X also bought Y and Z. Association models associate a particular conclusion
(such as the decision to buy something) with a set of conditions. The advantage of association rule
algorithms over the more standard decision tree algorithms (C5.0 and C&RT) is that associations
can exist between any of the attributes. A decision tree algorithm will build rules with only a
single conclusion, whereas association algorithms attempt to find many rules, each of which may
have a different conclusion.
Association nodes
The Apriori node extracts a set of rules from the data, pulling out the rules with
the highest information content. Apriori offers five different methods of selecting
rules and uses a sophisticated indexing scheme to process large data sets efficiently.
For large problems, Apriori is generally faster to train; it has no arbitrary limit on
the number of rules that can be retained, and it can handle rules with up to 32

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preconditions. Apriori requires that input and output fields all be categorical but
delivers better performance because it is optimized for this type of data.
The CARMA model extracts a set of rules from the data without requiring you to
specify input or target fields. In contrast to Apriori the CARMA node offers build
settings for rule support (support for both antecedent and consequent) rather than just
antecedent support. This means that the rules generated can be used for a wider variety
of applications—for example, to find a list of products or services (antecedents)
whose consequent is the item that you want to promote this holiday season.
The Sequence node discovers association rules in sequential or time-oriented data. A
sequence is a list of item sets that tends to occur in a predictable order. For example, a
customer who purchases a razor and aftershave lotion may purchase shaving cream
the next time he shops. The Sequence node is based on the CARMA association rules
algorithm, which uses an efficient two-pass method for finding sequences.

Segmentation Models

Segmentation models divide the data into segments, or clusters, of records that have similar
patterns of input fields. As they are only interested in the input fields, segmentation models have
no concept of output or target fields. Examples of segmentation models are Kohonen networks,
K-Means clustering, two-step clustering and anomaly detection.
Segmentation models (also known as “clustering models”) are useful in cases where the specific
result is unknown (for example, when identifying new patterns of fraud, or when identifying
groups of interest in your customer base). Clustering models focus on identifying groups of
similar records and labeling the records according to the group to which they belong. This is
done without the benefit of prior knowledge about the groups and their characteristics, and it
distinguishes clustering models from the other modeling techniques in that there is no predefined
output or target field for the model to predict. There are no right or wrong answers for these
models. Their value is determined by their ability to capture interesting groupings in the data and
provide useful descriptions of those groupings. Clustering models are often used to create clusters
or segments that are then used as inputs in subsequent analyses (for example, by segmenting
potential customers into homogeneous subgroups).

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Understanding Data Mining

Segmentation nodes
The Auto Cluster node estimates and compares clustering models, which identify
groups of records that have similar characteristics. The node works in the same
manner as other automated modeling nodes, allowing you to experiment with multiple
combinations of options in a single modeling pass. Models can be compared using
basic measures with which to attempt to filter and rank the usefulness of the cluster
models, and provide a measure based on the importance of particular fields.
The K-Means node clusters the data set into distinct groups (or clusters). The method
defines a fixed number of clusters, iteratively assigns records to clusters, and adjusts
the cluster centers until further refinement can no longer improve the model. Instead
of trying to predict an outcome, k-means uses a process known as unsupervised
learning to uncover patterns in the set of input fields.
The Kohonen node generates a type of neural network that can be used to cluster the
data set into distinct groups. When the network is fully trained, records that are
similar should be close together on the output map, while records that are different
will be far apart. You can look at the number of observations captured by each unit
in the model nugget to identify the strong units. This may give you a sense of the
appropriate number of clusters.
The TwoStep node uses a two-step clustering method. The first step makes a single
pass through the data to compress the raw input data into a manageable set of
subclusters. The second step uses a hierarchical clustering method to progressively
merge the subclusters into larger and larger clusters. TwoStep has the advantage of
automatically estimating the optimal number of clusters for the training data. It can
handle mixed field types and large data sets efficiently.
The Anomaly Detection node identifies unusual cases, or outliers, that do not conform
to patterns of “normal” data. With this node, it is possible to identify outliers even if
they do not fit any previously known patterns and even if you are not exactly sure
what you are looking for.

In-Database Mining Models

SPSS Modeler supports integration with data mining and modeling tools that are available from
database vendors, including Oracle Data Miner, IBM DB2 InfoSphere Warehouse, and Microsoft
Analysis Services. You can build, score, and store models inside the database—all from within the
SPSS Modeler application. For full details, see the SPSS Modeler In-Database Mining Guide,
available on the product DVD.
IBM SPSS Statistics Models

If you have a copy of IBM® SPSS® Statistics installed and licensed on your computer, you can
access and run certain SPSS Statistics routines from within SPSS Modeler to build and score
models.
Further Information

Detailed documentation on the modeling algorithms is also available. For more information, see
the SPSS Modeler Algorithms Guide, available on the product DVD.

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Chapter 4

Data Mining Examples
The best way to learn about data mining in practice is to start with an example. A number of
application examples are available in the IBM® SPSS® Modeler Applications Guide, which
provides brief, targeted introductions to specific modeling methods and techniques. For more
information, see the topic Application Examples in Chapter 1 on p. 5.

Chapter

Building Streams
Stream-Building Overview

Data mining using IBM® SPSS® Modeler focuses on the process of running data through a series
of nodes, referred to as a stream. This series of nodes represents operations to be performed on
the data, while links between the nodes indicate the direction of data flow. Typically, you use a
data stream to read data into SPSS Modeler, run it through a series of manipulations, and then
send it to a destination, such as a table or a viewer.
For example, suppose that you want to open a data source, add a new field, select records
based on values in the new field, and then display the results in a table. In this case, your data
stream would consist of four nodes:

A Variable File node, which you set up to read the data from the data source.

A Derive node, which you use to add the new, calculated field to the data set.

A Select node, which you use to set up selection criteria to exclude records from the
data stream.

A Table node, which you use to display the results of your manipulations onscreen.

Building Data Streams
IBM® SPSS® Modeler’s unique interface lets you mine your data visually by working with
diagrams of data streams. At the most basic level, you can build a data stream using the following
steps:

Add nodes to the stream canvas.

Connect the nodes to form a stream.

Specify any node or stream options.

Run the stream.

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Figure 5-1
Completed stream on the stream canvas

This section contains more detailed information on working with nodes to create more complex
data streams. It also discusses options and settings for nodes and streams. For step-by-step
examples of stream building using the data shipped with SPSS Modeler (in the Demos folder of
your program installation), see Application Examples on p. 5.

Working with Nodes
Nodes are used in IBM® SPSS® Modeler to help you explore data. Various nodes in the
workspace represent different objects and actions. The palette at the bottom of the SPSS Modeler
window contains all of the possible nodes used in stream building.
There are several types of nodes. Source nodes bring data into the stream, and are located on
the Sources tab of the nodes palette. Process nodes perform operations on individual data records
and fields, and can be found in the Record Ops and Field Ops tabs of the palette. Output nodes
produce a variety of output for data, charts and model results, and are included on the Graphs,
Output and Export tabs of the nodes palette. Modeling nodes use statistical algorithms to create
model nuggets, and are located on the Modeling tab, and (if activated) the Database Modeling tab,
of the nodes palette. For more information, see the topic Nodes Palette in Chapter 3 on p. 18.
You connect the nodes to form streams which, when run, let you visualize relationships and
draw conclusions. Streams are like scripts—you can save them and reuse them with different
data files.

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Building Streams

A runnable node that processes stream data is known as a terminal node. A modeling or
output node is a terminal node if it is located at the end of a stream or stream branch. You cannot
connect further nodes to a terminal node.
Note: You can customize the Nodes palette. For more information, see the topic Customizing
the Nodes Palette in Chapter 12 on p. 223.

Adding Nodes to a Stream
There are several ways to add nodes to a stream from the nodes palette:

Double-click a node on the palette. Note: Double-clicking a node automatically connects it
to the current stream. For more information, see the topic Connecting Nodes in a Stream
on p. 43.

Drag and drop a node from the palette to the stream canvas.

Click a node on the palette, and then click the stream canvas.

Select an appropriate option from the Insert menu of IBM® SPSS® Modeler.

Once you have added a node to the stream canvas, double-click the node to display its dialog box.
The available options depend on the type of node that you are adding. For information about
specific controls within the dialog box, click its Help button.
Removing Nodes

To remove a node from the data stream, click it and either press the Delete key, or right-click and
select Delete from the menu.

Connecting Nodes in a Stream
Nodes added to the stream canvas do not form a data stream until they have been connected.
Connections between the nodes indicate the direction of the data as it flows from one operation
to the next. There are a number of ways to connect nodes to form a stream: double-clicking,
using the middle mouse button, or manually.
To Add and Connect Nodes by Double-Clicking

The simplest way to form a stream is to double-click nodes on the palette. This method
automatically connects the new node to the selected node on the stream canvas. For example, if
the canvas contains a Database node, you can select this node and then double-click the next node
from the palette, such as a Derive node. This action automatically connects the Derive node to
the existing Database node. You can repeat this process until you have reached a terminal node,
such as a Histogram or Table node, at which point any new nodes will be connected to the last
non-terminal node upstream.

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Figure 5-2
Stream created by double-clicking nodes from the palettes

To Connect Nodes Using the Middle Mouse Button

On the stream canvas, you can click and drag from one node to another using the middle mouse
button. (If your mouse does not have a middle button, you can simulate this by pressing the Alt
key while dragging with the mouse from one node to another.)
Figure 5-3
Using the middle mouse button to connect nodes

To Manually Connect Nodes

If you do not have a middle mouse button and prefer to manually connect nodes, you can use the
pop-up menu for a node to connect it to another node already on the canvas.
E Right-click the node from which you want to start the connection. Doing so opens the node menu.
E On the menu, click Connect.
E A connection icon is displayed both on the start node and the cursor. Click a second node on

the canvas to connect the two nodes.
Figure 5-4
Connecting nodes using the Connect option from the pop-up menu

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Building Streams
Figure 5-5
Connected nodes

When connecting nodes, there are several guidelines to follow. You will receive an error message
if you attempt to make any of the following types of connections:

A connection leading to a source node

A connection leading from a terminal node

A node having more than its maximum number of input connections

Connecting two nodes that are already connected

Circularity (data returns to a node from which it has already flowed)

Bypassing Nodes in a Stream
When you bypass a node in the data stream, all of its input and output connections are replaced by
connections that lead directly from its input nodes to its output nodes. If the node does not have
both input and output connections, then all of its connections are deleted rather than rerouted.
For example, you might have a stream that derives a new field, filters fields, and then explores
the results in a histogram and table. If you want to also view the same graph and table for data
before fields are filtered, you can add either new Histogram and Table nodes to the stream, or you
can bypass the Filter node. When you bypass the Filter node, the connections to the graph and
table pass directly from the Derive node. The Filter node is disconnected from the stream.
Figure 5-6
Bypassing a previously connected Filter node

To Bypass a Node
E On the stream canvas, use the middle mouse button to double-click the node that you want to

bypass. Alternatively, you can use Alt+double-click.
Note: You can undo this action clicking Undo on the Edit menu or by pressing Ctrl+Z.

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Disabling Nodes in a Stream
Process nodes with a single input within streams can be disabled, with the result that the node is
ignored during running of the stream. This saves you from having to remove or bypass the node
and means you can leave it connected to the remaining nodes. You can still open and edit the node
settings; however, any changes will not take effect until you enable the node again.
For example, you might have a stream that filters several fields, and then builds models with
the reduced data set. If you want to also build the same models without fields being filtered, to
see if they improve the model results, you can disable the Filter node. When you disable the
Filter node, the connections to the modeling nodes pass directly through from the Derive node to
the Type node.
Figure 5-7
Disabled Filter node in a stream

To Disable a Node
E On the stream canvas, right-click the node that you want to disable.
E Click Disable Node on the pop-up menu.

Alternatively, you can click Node > Disable Node on the Edit menu. When you want to include the
node back in the stream, click Enable Node in the same way.
Note: You can undo this action clicking Undo on the Edit menu or by pressing Ctrl+Z.
You can undo this action clicking Undo on the Edit menu or by pressing Ctrl+Z.

Adding Nodes in Existing Connections
You can add a new node between two connected nodes by dragging the arrow that connects
the two nodes.

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Building Streams
Figure 5-8
Connecting a new node between two connected nodes

E With the middle mouse button, click and drag the connection arrow into which you want to insert

the node. Alternatively, you can hold down the Alt key while clicking and dragging to simulate a
middle mouse button.
Figure 5-9
New stream

E Drag the connection to the node that you want to include and release the mouse button.

Note: You can remove new connections from the node and restore the original by bypassing
the node.

Deleting Connections between Nodes
To delete the connection between two nodes:
E Right-click the connection arrow.
E On the menu, click Delete Connection.

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Figure 5-10
Deleting the connection between nodes in a stream

To delete all connections to and from a node, do one of the following:

Select the node and press F3.

Select the node, and on the main menu click:

Edit > Node > Disconnect

Setting Options for Nodes
Once you have created and connected nodes, there are several options for customizing nodes.
Right-click a node and select one of the menu options.

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Building Streams
Figure 5-11
Pop-up menu options for nodes

Click Edit to open the dialog box for the selected node.

Click Connect to manually connect one node to another.

Click Disconnect to delete all links to and from the node.

Click Rename and Annotate to open the Annotations tab of the editing dialog box.

Click New Comment to add a comment related to the node. For more information, see the
topic Adding Comments and Annotations to Nodes and Streams on p. 78.

Click Disable Node to “hide” the node during processing. To make the node visible again
for processing, click Enable Node. For more information, see the topic Disabling Nodes in
a Stream on p. 46.

Click Cut or Delete to remove the selected node(s) from the stream canvas. Note: Clicking Cut
allows you to paste nodes, while Delete does not.

Click Copy Node to make a copy of the node with no connections. This can be added to
a new or existing stream.

Click Load Node to open a previously saved node and load its options into the currently
selected node. Note: The nodes must be of identical types.

Click Retrieve Node to retrieve a node from a connected IBM® SPSS® Collaboration and
Deployment Services Repository.

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Chapter 5

Click Save Node to save the node’s details in a file. You can load node details only into
another node of the same type.

Click Store Node to store the selected node in a connected IBM SPSS Collaboration and
Deployment Services Repository.

Click Cache to expand the menu, with options for caching the selected node.

Click Data Mapping to expand the menu, with options for mapping data to a new source or
specifying mandatory fields.

Click Create SuperNode to expand the menu, with options for creating a SuperNode in the
current stream.

Click Generate User Input Node to replace the selected node. Examples generated by this node
will have the same fields as the current node.

Click Run From Here to run all terminal nodes downstream from the selected node.

Caching Options for Nodes
To optimize stream running, you can set up a cache on any nonterminal node. When you set up a
cache on a node, the cache is filled with the data that passes through the node the next time you
run the data stream. From then on, the data is read from the cache (which is stored on disk in a
temporary directory) rather than from the data source.
Caching is most useful following a time-consuming operation such as a sort, merge, or
aggregation. For example, suppose that you have a source node set to read sales data from a
database and an Aggregate node that summarizes sales by location. You can set up a cache on the
Aggregate node rather than on the source node because you want the cache to store the aggregated
data rather than the entire data set.
Note: Caching at source nodes, which simply stores a copy of the original data as it is read into
IBM® SPSS® Modeler, will not improve performance in most circumstances.
Nodes with caching enabled are displayed with a small document icon at the top right corner.
When the data is cached at the node, the document icon is green.

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Building Streams
Figure 5-12
Caching at the Type node to store newly derived fields

To Enable a Cache
E On the stream canvas, right-click the node and click Cache on the menu.
E On the caching submenu, click Enable.
E You can turn the cache off by right-clicking the node and clicking Disable on the caching submenu.

Caching Nodes in a Database

For streams run in a database, data can be cached midstream to a temporary table in the database
rather than the file system. When combined with SQL optimization, this may result in significant
gains in performance. For example, the output from a stream that merges multiple tables to create
a data mining view may be cached and reused as needed. By automatically generating SQL for
all downstream nodes, performance can be further improved.
When using database caching with strings longer than 255 characters, either ensure that there
is a Type node upstream from the caching node and that the field values are read, or set the
string length by means of the default_sql_string_length parameter in the options.cfg file. Doing
so ensures that the corresponding column in the temporary table is set to the correct width to
accommodate the strings.
To take advantage of database caching, both SQL optimization and database caching must
be enabled. Note that Server optimization settings override those on the Client. For more
information, see the topic Setting optimization options for streams on p. 60.
With database caching enabled, simply right-click any nonterminal node to cache data at
that point, and the cache will be created automatically directly in the database the next time the
stream is run. If database caching or SQL optimization is not enabled, the cache will be written
to the file system instead.

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Note: The following databases support temporary tables for the purpose of caching: DB2,
Netezza, Oracle, SQL Server, and Teradata. Other databases will use a normal table for database
caching. The SQL code can be customized for specific databases - contact Support for assistance.
To Flush a Cache

A white document icon on a node indicates that its cache is empty. When the cache is full, the
document icon becomes solid green. If you want to replace the contents of the cache, you must
first flush the cache and then re-run the data stream to refill it.
E On the stream canvas, right-click the node and click Cache on the menu.
E On the caching submenu, click Flush.

To Save a Cache

You can save the contents of a cache as an IBM® SPSS® Statistics data file (*.sav). You can then
either reload the file as a cache, or you can set up a node that uses the cache file as its data source.
You can also load a cache that you saved from another project.
E On the stream canvas, right-click the node and click Cache on the menu.
E On the caching submenu, click Save Cache.
E In the Save Cache dialog box, browse to the location where you want to save the cache file.
E Enter a name in the File Name text box.
E Be sure that *.sav is selected in the Files of Type list, and click Save.

To Load a Cache

If you have saved a cache file before removing it from the node, you can reload it.
E On the stream canvas, right-click the node and click Cache on the menu.
E On the caching submenu, click Load Cache.
E In the Load Cache dialog box, browse to the location of the cache file, select it, and click Load.

Previewing Data in Nodes
To ensure that data is being changed in the way you expect as you build a stream, you could run
your data through a Table node at each significant step. To save you from having to do this you
can generate a preview from each node that displays a sample of the data that will be created,
thereby reducing the time it takes to build each node.
For nodes upstream of a model nugget, the preview shows the input fields; for a model
nugget or nodes downstream of the nugget (except terminal nodes), the preview shows input
and generated fields.
The default number of rows displayed is 10; however, you can change this in the stream
properties. For more information, see the topic Setting general options for streams on p. 55.

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Building Streams
Figure 5-13
Data Preview from a model nugget

From the Generate menu, you can create several types of nodes.

Locking Nodes
To prevent other users from amending the settings of one or more nodes in a stream, you can
encapsulate the node or nodes in a special type of node called a SuperNode, and then lock the
SuperNode by applying password protection.

Working with Streams
Once you have connected source, process, and terminal nodes on the stream canvas, you have
created a stream. As a collection of nodes, streams can be saved, annotated, and added to projects.
You can also set numerous options for streams, such as optimization, date and time settings,
parameters, and scripts. These properties are discussed in the topics that follow.
In IBM® SPSS® Modeler, you can use and modify more than one data stream in the same
SPSS Modeler session. The right side of the main window contains the managers pane, which
helps you to navigate the streams, outputs and models that are currently open. If you cannot see
the managers pane, click Managers on the View menu, then click the Streams tab.

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Chapter 5
Figure 5-14
Streams tab in the managers pane with pop-up menu options

From this tab, you can:

Access streams.

Save streams.

Save streams to the current project.

Close streams.

Open new streams.

Store and retrieve streams from an IBM SPSS Collaboration and Deployment Services
repository (if available at your site). For more information, see the topic About the IBM SPSS
Collaboration and Deployment Services Repository in Chapter 9 on p. 158.

Right-click a stream on the Streams tab to access these options.

Setting Options for Streams
You can specify a number of options to apply to the current stream. You can also save these
options as defaults to apply to all your streams. The options are as follows.

General. Miscellaneous options such as symbols and text encoding to use in the stream. For
more information, see the topic Setting general options for streams on p. 55.

Date/Time. Options relating to the format of date and time expressions. For more information,
see the topic Setting date and time options for streams on p. 57.

Number formats. Options controlling the format of numeric expressions. For more information,
see the topic Setting number format options for streams on p. 59.

Optimization. Options for optimizing stream performance. For more information, see the
topic Setting optimization options for streams on p. 60.

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Building Streams

Logging and status. Options controlling SQL logging and record status. For more information,
see the topic Setting SQL logging and record status options for streams on p. 63.

Layout. Options relating to the layout of the stream on the canvas. For more information, see
the topic Setting layout options for streams on p. 64.

To Set Stream Options
E On the File menu, click Stream Properties (or select the stream from the Streams tab in the
managers pane, right-click and then click Stream Properties on the pop-up menu).
E Click the Options tab.

Alternatively, on the Tools menu, click:
Stream Properties > Options

Setting general options for streams
The general options are a set of miscellaneous options that apply to various aspects of the current
stream.

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Chapter 5
Figure 5-15
Setting general options for a stream

Decimal symbol. Select either a comma (,) or a period (.) as a decimal separator.
Grouping symbol. For number display formats, select the symbol used to group values (for example,
the comma in 3,000.00). Options include none, period, comma, space, and locale-defined (in
which case the default for the current locale is used).
Encoding. Specify the stream default method for text encoding. (Note: Applies to Var. File
source node and Flat File export node only. No other nodes use this setting; most data files have
embedded encoding information.) You can choose either the system default or UTF-8. The
system default is specified in the Windows Control Panel or, if running in distributed mode, on the
server computer. For more information, see the topic Unicode Support in IBM SPSS Modeler
in Appendix B on p. 248.
Ruleset Evaluation. Determines how rule set models are evaluated. By default, rule sets use Voting
to combine predictions from individual rules and determine the final prediction. To ensure that
rule sets use the first hit rule by default, select First Hit. Note that this option does not apply to
Decision List models, which always use the first hit as defined by the algorithm.

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Maximum number of rows to show in Data Preview. Specify the number of rows to be shown when
a preview of the data is requested for a node. For more information, see the topic Previewing
Data in Nodes on p. 52.
Maximum members for nominal fields. Select to specify a maximum number of members for
nominal (set) fields after which the data type of the field becomes Typeless. This option is useful
when working with large nominal fields. Note: When the measurement level of a field is set to
Typeless, its role is automatically set to None. This means that the fields are not available for
modeling.
Limit set size for Kohonen, and K-Means modeling. Select to specify a maximum number of members
for nominal fields used in Kohonen nets and K-Means modeling. The default set size is 20, after
which the field is ignored and a warning is raised, providing information on the field in question.

Note that, for compatibility, this option also applies to the old Neural Network node that was
replaced in version 14 of IBM® SPSS® Modeler; some legacy streams may still contain this node.
Refresh source nodes on execution. Select to automatically refresh all source nodes when running
the current stream. This action is analogous to clicking the Refresh button on a source node,
except that this option automatically refreshes all source nodes (except User Input nodes) for the
current stream.
Note: Selecting this option flushes the caches of downstream nodes even if the data has not
changed. Flushing occurs only once per running of the stream, though, which means that you can
still use downstream caches as temporary storage for a single running. For example, say that you
have set a cache midstream after a complex derive operation and that you have several graphs
and reports attached downstream of this Derive node. When running the stream, the cache at the
Derive node will be flushed and refilled but only for the first graph or report. Subsequent terminal
nodes will read data from the Derive node cache.
Display field and value labels in output. Displays field and value labels in tables, charts, and other
output. If labels do not exist, the field names and data values will be displayed instead. Labels are
turned off by default; however, you can toggle labels on an individual basis elsewhere in SPSS
Modeler. You can also choose to display labels on the output window using a toggle button
available on the toolbar.
Figure 5-16
Toolbar icon used to toggle field and value labels

Display execution times. Displays individual execution times for stream nodes on the Execution
Times tab after the stream is run. For more information, see the topic Viewing Node Execution
Times on p. 67.
Save As Default. The options specified apply only to the current stream. Click this button to set
these options as the default for all streams.

Setting date and time options for streams
These options specify the format to use for various date and time expressions in the current stream.

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Figure 5-17
Setting date and time options for a stream

Import date/time as. Select whether to use date/time storage for date/time fields or whether
to import them as string variables.
Date format. Select a date format to be used for date storage fields or when strings are interpreted
as dates by CLEM date functions.
Time format. Select a time format to be used for time storage fields or when strings are interpreted
as times by CLEM time functions.
Rollover days/mins. For time formats, select whether negative time differences should be
interpreted as referring to the previous day or hour.
Date baseline (1st Jan). Select the baseline years (always 1 January) to be used by CLEM date
functions that work with a single date.

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2-digit dates start from. Specify the cutoff year to add century digits for years denoted with only
two digits. For example, specifying 1930 as the cutoff year will assume that 05/11/02 is in the
year 2002. The same setting will use the 20th century for dates after 30; thus 05/11/73 is assumed
to be in 1973.
Save As Default. The options specified apply only to the current stream. Click this button to set
these options as the default for all streams.

Setting number format options for streams
These options specify the format to use for various numeric expressions in the current stream.
Figure 5-18
Setting number format options for a stream

Number display format. You can choose from standard (####.###), scientific (#.###E+##), or
currency display formats ($###.##).

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Decimal places (standard, scientific, currency). For number display formats, specifies the number
of decimal places to be used when displaying or printing real numbers. This option is specified
separately for each display format.
Calculations in. Select Radians or Degrees as the unit of measurement to be used in trigonometric
CLEM expressions. For more information, see the topic Trigonometric Functions in Chapter 8
on p. 139.
Save As Default. The options specified apply only to the current stream. Click this button to set
these options as the default for all streams.

Setting optimization options for streams
You can use the Optimization settings to optimize stream performance. Note that the performance
and optimization settings on IBM® SPSS® Modeler Server (if used) override any equivalent
settings in the client.
Note: Database modeling and SQL optimization require that SPSS Modeler Server connectivity
be enabled on the IBM® SPSS® Modeler computer. With this setting enabled, you can access
database algorithms, push back SQL directly from SPSS Modeler, and access SPSS Modeler
Server. To verify the current license status, choose the following from the SPSS Modeler menu.
Help > About > Additional Details

If connectivity is enabled, you see the option Server Enablement in the License Status tab.
For more information, see the topic Connecting to IBM SPSS Modeler Server in Chapter 3 on
p. 13.

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Building Streams
Figure 5-19
Setting stream optimization options

Note: Whether SQL pushback and optimization are supported depends on the type of database in
use. For the latest information on which databases and ODBC drivers are supported and tested for
use with IBM® SPSS® Modeler 15, see the corporate Support site at http://www.ibm.com/support.
Enable stream rewriting. Select this option to enable stream rewriting in SPSS Modeler. Two types
of rewriting are available, and you can select one or both. Stream rewriting reorders the nodes in a
stream behind the scenes for more efficient operation, without altering stream semantics.

Optimize SQL generation. This option enables nodes to be reordered within the stream so that
more operations can be pushed back using SQL generation for execution in the database.
When it finds a node that cannot be rendered into SQL, the optimizer will look ahead to
see if there are any downstream nodes that can be rendered into SQL and safely moved in
front of the problem node without affecting the stream semantics. Not only can the database
perform operations more efficiently than SPSS Modeler, but such pushbacks act to reduce
the size of the data set that is returned to SPSS Modeler for processing. This, in turn, can

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reduce network traffic and speed stream operations. Note that the Generate SQL check box
must be selected for SQL optimization to have any effect.

Optimize syntax execution. This method of stream rewriting increases the efficiency of
operations that incorporate more than one node containing IBM® SPSS® Statistics syntax.
Optimization is achieved by combining the syntax commands into a single operation, instead
of running each as a separate operation.

Optimize other execution. This method of stream rewriting increases the efficiency of
operations that cannot be delegated to the database. Optimization is achieved by reducing the
amount of data in the stream as early as possible. While maintaining data integrity, the stream
is rewritten to push operations closer to the data source, thus reducing data downstream for
costly operations, such as joins.

Enable parallel processing. When running on a computer with multiple processors, this option
allows the system to balance the load across those processors, which may result in faster
performance. Use of multiple nodes or use of the following individual nodes may benefit from
parallel processing: C5.0, Merge (by key), Sort, Bin (rank and tile methods), and Aggregate
(using one or more key fields).
Generate SQL. Select this option to enable SQL generation, allowing stream operations to be pushed
back to the database by using SQL code to generate execution processes, which may improve
performance. To further improve performance, Optimize SQL generation can also be selected to
maximize the number of operations pushed back to the database. When operations for a node have
been pushed back to the database, the node will be highlighted in purple when the stream is run.

Database caching. For streams that generate SQL to be executed in the database, data can be
cached midstream to a temporary table in the database rather than to the file system. When
combined with SQL optimization, this may result in significant gains in performance. For
example, the output from a stream that merges multiple tables to create a data mining view
may be cached and reused as needed. With database caching enabled, simply right-click any
nonterminal node to cache data at that point, and the cache is automatically created directly in
the database the next time the stream is run. This allows SQL to be generated for downstream
nodes, further improving performance. Alternatively, this option can be disabled if needed,
such as when policies or permissions preclude data being written to the database. If database
caching or SQL optimization is not enabled, the cache will be written to the file system
instead. For more information, see the topic Caching Options for Nodes on p. 50.

Use relaxed conversion. This option enables the conversion of data from either strings to
numbers, or numbers to strings, if stored in a suitable format. For example, if the data is
kept in the database as a string, but actually contains a meaningful number, the data can be
converted for use when the pushback occurs.

Note: Due to minor differences in SQL implementation, streams run in a database may return
slightly different results from those returned when run in SPSS Modeler. For similar reasons, these
differences may also vary depending on the database vendor.
Save As Default. The options specified apply only to the current stream. Click this button to set
these options as the default for all streams.

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Setting SQL logging and record status options for streams
These settings include various options controlling the display of SQL statements generated by the
stream, and the display of the number of records processed by the stream.
Figure 5-20
Setting SQL logging and record status options for a stream

Display SQL in the messages log during stream execution. Specifies whether SQL generated while
running the stream is passed to the message log.
Display SQL generation details in the messages log during stream preparation. During stream
preview, specifies whether a preview of the SQL that would be generated is passed to the
messages log.
Display SQL. Specifies whether any SQL that is displayed in the log should contain native SQL
functions or standard ODBC functions of the form {fn FUNC(…)}, as generated by IBM® SPSS®
Modeler. The former relies on ODBC driver functionality that may not be implemented. For
example, this control would have no effect for SQL Server.

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Reformat SQL for improved readability. Specifies whether SQL displayed in the log should be
formatted for readability.
Show status for records. Specifies when records should be reported as they arrive at terminal
nodes. Specify a number that is used for updating the status every N records.
Save As Default. The options specified apply only to the current stream. Click this button to set
these options as the default for all streams.

Setting layout options for streams
These settings provide a number of options relating to the display and use of the stream canvas.
Figure 5-21
Setting display layout options for a stream

Minimum stream canvas width. Specify the minimum width of the stream canvas in pixels.
Minimum stream canvas height. Specify the minimum height of the stream canvas in pixels.

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Building Streams

Stream scroll rate. Specify the scrolling rate for the stream canvas to control how quickly the
stream canvas pane scrolls when a node is being dragged from one place to another on the canvas.
Higher numbers specify a faster scroll rate.
Icon name maximum. Specify a limit in characters for the names of nodes on the stream canvas.
Icon size. Select an option to scale the entire stream view to one of a number of sizes between 8%
and 200% of the standard icon size.
Grid cell size. Select a grid cell size from the list. This number is used for aligning nodes on the
stream canvas using an invisible grid. The default grid cell size is 0.25.
Snap to Grid. Select to align icons to an invisible grid pattern (selected by default).
Generated icon placement. Choose where on the canvas to place icons for nodes generated from
model nuggets. Default is top left.
Save As Default. The options specified apply only to the current stream. Click this button to set
these options as the default for all streams.

Viewing Stream Operation Messages
Messages regarding stream operations, such as running, optimization, and time elapsed for model
building and evaluation, can easily be viewed using the Messages tab in the stream properties
dialog box. Error messages are also reported in this table.
To View Stream Messages
E On the File menu, click Stream Properties (or select the stream from the Streams tab in the
managers pane, right-click and then click Stream Properties on the pop-up menu).
E Click the Messages tab.

Alternatively, on the Tools menu, click:
Stream Properties > Messages

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Figure 5-22
Messages tab in stream properties dialog box

In addition to messages regarding stream operations, error messages are reported here. When
stream running is terminated because of an error, this dialog box will open to the Messages tab
with the error message visible. Additionally, the node with errors is highlighted in red on the
stream canvas.

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Building Streams
Figure 5-23
Stream running with error reported

If SQL optimization and logging options are enabled in the User Options dialog box, then
information on generated SQL is also displayed. For more information, see the topic Setting
optimization options for streams on p. 60.
You can save messages reported here for a stream by clicking Save Messages on the Save
button drop-down list (on the left, just below the Messages tab). You can also clear all messages
for a given stream by clicking Clear All Messages on the Save button list.

Viewing Node Execution Times
On the Messages tab you can also choose to display Execution Times, where you can see the
individual execution times for all the nodes in the stream.
Note: For this feature to work, the Display execution times check box must be selected on the
General setting of the Options tab.

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Figure 5-24
Viewing execution times for nodes in the stream

In the table of node execution times, the columns are as follows. Click a column heading to sort
the entries into ascending or descending order (for example, to see which nodes have the longest
execution times).
Terminal Node. The identifier of the branch to which the node belongs. The identifier is the name
of the terminal node at the end of the branch.
Node Label. The name of the node to which the execution time refers.
Node Id. The unique identifier of the node to which the execution time refers. This identifier is
generated by the system when the node is created.
Execution Time(s). The time in seconds taken to execute this node.

Setting Stream and Session Parameters
Parameters can be defined for use in CLEM expressions and in scripting. They are, in effect,
user-defined variables that are saved and persisted with the current stream, session, or SuperNode
and can be accessed from the user interface as well as through scripting. If you save a stream, for
example, any parameters set for that stream are also saved. (This distinguishes them from local
script variables, which can be used only in the script in which they are declared.) Parameters are
often used in scripting as part of a CLEM expression in which the parameter value is specified in
the script.
The scope of a parameter depends on where it is set:

Stream parameters can be set in a stream script or in the stream properties dialog box, and
they are available to all nodes in the stream. They are displayed on the Parameters list in the
Expression Builder.

Session parameters can be set in a stand-alone script or in the session parameters dialog
box. They are available to all streams used in the current session (all streams listed on the
Streams tab in the managers pane).

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Parameters can also be set for SuperNodes, in which case they are visible only to nodes
encapsulated within that SuperNode.
To Set Stream and Session Parameters through the User Interface
E To set stream parameters, on the main menu, click:
Tools > Stream Properties > Parameters
E To set session parameters, click Set Session Parameters on the Tools menu.
Figure 5-25
Setting parameters for the session

Prompt?. Check this box if you want the user to be prompted at runtime to enter a value for
this parameter.
Name. Parameter names are listed here. You can create a new parameter by entering a name in this
field. For example, to create a parameter for the minimum temperature, you could type minvalue.
Do not include the $P- prefix that denotes a parameter in CLEM expressions. This name is also
used for display in the CLEM Expression Builder.
Long name. Lists the descriptive name for each parameter created.
Storage. Select a storage type from the list. Storage indicates how the data values are stored in
the parameter. For example, when working with values containing leading zeros that you want
to preserve (such as 008), you should select String as the storage type. Otherwise, the zeros
will be stripped from the value. Available storage types are string, integer, real, time, date, and
timestamp. For date parameters, note that values must be specified using ISO standard notation as
shown in the next paragraph.
Value. Lists the current value for each parameter. Adjust the parameter as required. Note that for
date parameters, values must be specified in ISO standard notation (that is, YYYY-MM-DD). Dates
specified in other formats are not accepted.
Type (optional). If you plan to deploy the stream to an external application, select a measurement
level from the list. Otherwise, it is advisable to leave the Type column as is. If you want to
specify value constraints for the parameter, such as upper and lower bounds for a numeric range,
select Specify from the list.

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Note that long name, storage, and type options can be set for parameters through the user
interface only. These options cannot be set using scripts.
Click the arrows at the right to move the selected parameter further up or down the list of
available parameters. Use the delete button (marked with an X) to remove the selected parameter.

Specifying Runtime Prompts for Parameter Values
If you have streams where you might need to enter different values for the same parameter on
different occasions, you can specify runtime prompts for one or more stream or session parameter
values.
Figure 5-26
Runtime prompting for parameter values

Parameters. (Optional) Enter a value for the parameter, or leave the default value if there is one.
Turn off these prompts. Select this box if you do not want these prompts to be displayed when you
run the stream. You can cause them to be redisplayed by selecting the Prompt? check box on the
stream properties or session properties dialog box where the parameters were defined. For more
information, see the topic Setting Stream and Session Parameters on p. 68.

Specifying Value Constraints for a Parameter Type
You can make value constraints for a parameter available during stream deployment to an external
application that reads data modeling streams. This dialog box allows you to specify the values
available to an external user running the stream. Depending on the data type, value constraints
vary dynamically in the dialog box. The options shown here are identical to the options available
for values from the Type node.

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Figure 5-27
Specifying available values for a parameter

Type. Displays the currently selected measurement level. You can change this value to reflect the
way that you intend to use the parameter in IBM® SPSS® Modeler.
Storage. Displays the storage type if known. Storage types are unaffected by the measurement
level (continuous, nominal or flag) that you choose for work in SPSS Modeler. You can alter
the storage type on the main Parameters tab.

The bottom half of the dialog box dynamically changes depending on the measurement level
selected in the Type field.
Continuous Measurement Levels
Lower. Specify a lower limit for the parameter values.
Upper. Specify an upper limit for the parameter values.
Labels. You can specify labels for any value of a range field. Click the Labels button to open a
separate dialog box for specifying value labels.
Nominal Measurement Levels
Values. This option allows you to specify values for a parameter that will be used as a nominal
field. Values will not be coerced in the SPSS Modeler stream but will be used in a drop-down
list for external deployment applications. Using the arrow and delete buttons, you can modify
existing values as well as reorder or delete values.
Flag Measurement Levels
True. Specify a flag value for the parameter when the condition is met.
False. Specify a flag value for the parameter when the condition is not met.
Labels. You can specify labels for the values of a flag field.

Stream Deployment Options
The Deployment tab of the stream properties dialog box enables you to specify options for
deploying the stream as a scenario within IBM® SPSS® Collaboration and Deployment
Services for the purposes of model refresh, automated job scheduling, or further use by IBM®

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Analytical Decision Management or Predictive Applications 5.x. All streams require a designated
scoring branch before they can be deployed; additional requirements and options depend on the
deployment type. For more information, see the topic Storing and Deploying Repository Objects
in Chapter 9 on p. 160.

Viewing Global Values for Streams
Using the Globals tab in the stream properties dialog box, you can view the global values set for
the current stream. Global values are created using a Set Globals node to determine statistics such
as mean, sum, or standard deviation for selected fields.
Once the Set Globals node is run, these values are then available for a variety of uses in stream
operations. For more information, see the topic Global Functions in Chapter 8 on p. 155.

To View Global Values for a Stream
E On the File menu, click Stream Properties (or select the stream from the Streams tab in the
managers pane, right-click and then click Stream Properties on the pop-up menu).
E Click the Globals tab.

Alternatively, on the Tools menu, click:
Stream Properties > Globals
Figure 5-28
Viewing global values available for the stream

Globals available. Available globals are listed in this table. You cannot edit global values here,
but you can clear all global values for a stream using the Clear All Values button to the right of
the table.

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Searching for Nodes in a Stream
You can search for nodes in a stream by specifying a number of search criteria, such as node name,
category and identifier. This feature can be especially useful for complex streams containing a
large number of nodes.
To Search for Nodes in a Stream
E On the File menu, click Stream Properties (or select the stream from the Streams tab in the
managers pane, right-click and then click Stream Properties on the pop-up menu).
E Click the Search tab.

Alternatively, on the Tools menu, click:
Stream Properties > Search
Figure 5-29
Searching for nodes in a stream

You can specify more than one option to limit the search, except that searching by node ID (using
the ID equals field) excludes the other options.
Node label contains. Check this box and enter all or part of a node label to search for a particular
node. Searches are not case-sensitive, and multiple words are treated as a single piece of text.

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Node category. Check this box and click a category on the list to search for a particular type of
node. Process Node means a node from the Record Ops or Field Ops tab of the nodes palette;
Apply Model Node refers to a model nugget.
Keywords include. Check this box and enter one or more complete keywords to search for nodes
having that text in the Keywords field on the Annotations tab of the node dialog box. Keyword
text that you enter must be an exact match. Separate multiple keywords with semicolons to search
for alternatives (for example, entering proton;neutron will find all nodes with either of these
keywords. For more information, see the topic Annotations on p. 86.
Annotation contains. Check this box and enter one or more words to search for nodes that contain
this text in the main text area on the Annotations tab of the node dialog box. Searches are not
case-sensitive, and multiple words are treated as a single piece of text. For more information,
see the topic Annotations on p. 86.
Generates field called. Check this box and enter the name of a generated field (for example,
$C-Drug). You can use this option to search for modeling nodes that generate a particular field.
Enter only one field name, which must be an exact match.
ID equals. Check this box and enter a node ID to search for a particular node with that identifier
(selecting this option disables all the preceding options). Node IDs are assigned by the system
when the node is created, and can be used to reference the node for the purposes of scripting or
automation. Enter only one node ID, which must be an exact match. For more information,
see the topic Annotations on p. 86.
Search in SuperNodes. This box is checked by default, meaning that the search is performed on
nodes both inside and outside SuperNodes. Clear the box if you want to perform the search only
on nodes outside SuperNodes, at the top level of the stream.
Find. When you have specified all the options you want, click this button to start the search.

Nodes that match the specified options are listed in the lower part of the dialog box. Select a node
in the list to highlight it on the stream canvas.

Renaming Streams
Using the Annotations tab in the stream properties dialog box, you can add descriptive annotations
for a stream and create a custom name for the stream. These options are useful especially
when generating reports for streams added to the project pane. For more information, see the
topic Annotations on p. 86.

Stream Descriptions
For each stream that you create, IBM® SPSS® Modeler produces a stream description containing
information on the contents of the stream. This can be useful if you are trying to see what a stream
does but you do not have SPSS Modeler installed, for example when accessing a stream through
IBM® SPSS® Collaboration and Deployment Services.

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Building Streams
Figure 5-30
Opening section of stream description

The stream description is displayed in the form of an HTML document consisting of a number
of sections.
General Stream Information

This section contains the stream name, together with details of when the stream was created
and last saved.
Description and Comments

This section includes any:

Stream annotations (see Annotations on p. 86)

Comments not connected to specific nodes

Comments connected to nodes in both the modeling and scoring branches of the stream

Scoring Information

This section contains information under various headings relating to the scoring branch of the
stream.

Comments. Includes comments linked only to nodes in the scoring branch.

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Inputs. Lists the input fields together with their storage types (for example, string, integer,
real and so on).

Outputs. Lists the output fields, including the additional fields generated by the modeling node,
together with their storage types.

Parameters. Lists any parameters relating to the scoring branch of the stream and which can be
viewed or edited each time the model is scored. These parameters are identified when you
click the Scoring Parameters button on the Deployment tab of the stream properties dialog box.

Model Node. Shows the model name and type (for example, Neural Net, C&R Tree and so
on). This is the model nugget selected for the Model node field on the Deployment tab of
the stream properties dialog box.

Model Details. Shows details of the model nugget identified under the previous heading.
Where possible, predictor importance and evaluation charts for the model are included.

Modeling Information

Contains information relating to the modeling branch of the stream.

Comments. Lists any comments or annotations that are connected to nodes in the modeling
branch.

Inputs. Lists the input fields together with their role in the modeling branch (in the form of the
field role value, for example, Input, Target, Split and so on).

Parameters. Lists any parameters relating to the modeling branch of the stream and which can
be viewed or edited each time the model is updated. These parameters are identified when
you click the Model Build Parameters button on the Deployment tab of the stream properties
dialog box.

Modeling node. Shows the name and type of the modeling node used to generate or update
the model.

Previewing Stream Descriptions
You can view the contents of a stream description in a web browser by clicking an option on the
stream properties dialog box. The contents of the description depend on the options you specify
on the Deployment tab of the dialog box. For more information, see the topic Stream Deployment
Options in Chapter 9 on p. 185.
To view a stream description:
E On the main IBM® SPSS® Modeler menu, click:
Tools > Stream Properties > Deployment
E Set the deployment type, the designated scoring node and any scoring parameters.
E If the deployment type is Model Refresh, you can optionally select a:

Modeling node and any model build parameters

Model nugget on the scoring branch of the stream

E Click the Preview Stream Description button.

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Building Streams

Exporting Stream Descriptions
You can export the contents of the stream description to an HTML file.
To export a stream description:
E On the main menu, click:
File > Export Stream Description
E Enter a name for the HTML file and click Save.

Running Streams
Once you have specified the required options for streams and connected the required nodes, you
can run the stream by running the data through nodes in the stream. There are several ways to run
a stream within IBM® SPSS® Modeler. You can:

Click Run on the Tools menu.

Click one of the Run... buttons on the toolbar. These buttons allow you to run the entire
stream or simply the selected terminal node. For more information, see the topic IBM SPSS
Modeler Toolbar in Chapter 3 on p. 21.

Run a single data stream by right-clicking a terminal node and clicking Run on the pop-up
menu.

Run part of a data stream by right-clicking any non-terminal node and clicking Run From
Here on the pop-up menu. Doing so causes only those operations after the selected node to
be performed.

To halt the running of a stream in progress, you can click the red Stop button on the toolbar, or
click Stop Execution on the Tools menu.
If any stream takes longer than three seconds to run, the Execution Feedback dialog box is
displayed to indicate the progress.
Figure 5-31
Execution Feedback dialog box

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Some nodes have further displays giving additional information about stream execution. These
are displayed by selecting the corresponding row in the dialog box. The first row is selected
automatically.

Working with Models
If a stream includes a modeling node (that is, one from the Modeling or Database Modeling tab
of the nodes palette), a model nugget is created when the stream is run. A model nugget is a
container for a model, that is, the set of rules, formulas or equations that enables you to generate
predictions against your source data, and which lies at the heart of predictive analytics.
Figure 5-32
Model nugget

When you successfully run a modeling node, a corresponding model nugget is placed on the
stream canvas, where it is represented by a gold diamond-shaped icon (hence the name “nugget”).
You can open the nugget and browse its contents to view details about the model. To view the
predictions, you attach and run one or more terminal nodes, the output from which presents
the predictions in a readable form.
Figure 5-33
Modeling and scoring branches in a stream

A typical modeling stream consists of two branches. The modeling branch contains the modeling
node, together with the source and processing nodes that precede it. The scoring branch is
created when you run the modeling node, and contains the model nugget and the terminal node or
nodes that you use to view the predictions.
For more information, see the IBM® SPSS® Modeler Modeling Nodes guide.

Adding Comments and Annotations to Nodes and Streams
You may need to describe a stream to others in your organization. To help you do this, you can
attach explanatory comments to streams, nodes and model nuggets.

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Figure 5-34
Stream with comments added

Others can then view these comments on-screen, or you can print out an image of the stream
that includes the comments.
You can list all the comments for a stream or SuperNode, change the order of comments in the
list, edit the comment text, and change the foreground or background color of a comment. For
more information, see the topic Listing Stream Comments on p. 84.
You can also add notes in the form of text annotations to streams, nodes and nuggets by
means of the Annotations tab of a stream properties dialog box, a node dialog box, or a model
nugget window. These notes are visible only when the Annotations tab is open, except that
stream annotations can also be shown as on-screen comments. For more information, see the
topic Annotations on p. 86.

Comments
Comments take the form of text boxes in which you can enter any amount of text, and you can
add as many comments as you like. A comment can be freestanding (not attached to any stream
objects), or it can be connected to one or more nodes or model nuggets in the stream. Freestanding
comments are typically used to describe the overall purpose of the stream; connected comments
describe the node or nugget to which they are attached. Nodes and nuggets can have more than
one comment attached, and the stream can have any number of freestanding comments.
Note: You can also show stream annotations as on-screen comments, though these cannot be
attached to nodes or nuggets. For more information, see the topic Converting Annotations to
Comments on p. 85.

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The appearance of the text box changes to indicate the current mode of the comment (or annotation
shown as a comment), as the following table shows.
Table 5-1
Comment and annotation text box modes

Comment text box Annotation text
box

Mode Indicates

Obtained by...

Edit

Creating a new comment
or annotation, or
double-clicking an
existing one.
Clicking the stream
background after editing,
or single-clicking an
existing comment or
annotation.
Clicking on another node,
comment or annotation
after editing.

Comment is open for
editing.

Last
Comment can be moved,
selected resized or deleted.

View

Editing is complete.

When you create a new freestanding comment, it is initially displayed in the top left corner
of the stream canvas.
Figure 5-35
New freestanding comment

If you are attaching a comment to a node or nugget, the comment is initially displayed above the
stream object to which it is attached.
Figure 5-36
New comment attached to node

The text box is colored white to show that text can be entered. When you have entered the text,
you click outside the text box. The comment background changes to yellow to show that text entry
is complete. The comment remains selected, allowing you to move, resize, or delete it.

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Figure 5-37
Comment in edit mode

When you click again, the border changes to solid lines to show that editing is complete.
Figure 5-38
Completed comment

Double-clicking a comment changes the text box to edit mode—the background changes
to white and the comment text can be edited.
You can also attach comments to SuperNodes.

Operations Involving Comments
You can perform a number of operations on comments. You can:

Add a freestanding comment

Attach a comment to a node or nugget

Edit a comment

Resize a comment

Move a comment

Disconnect a comment

Delete a comment

Show or hide all comments for a stream

To add a freestanding comment
E Ensure that nothing is selected on the stream.
E Do one of the following:

On the main menu, click:

Insert > New Comment

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Right-click the stream background and click New Comment on the pop-up menu.

Click the New Comment button in the toolbar.

E Enter the comment text (or paste in text from the clipboard).
E Click a node in the stream to save the comment.

To attach a comment to a node or nugget
E Select one or more nodes or nuggets on the stream canvas.
E Do one of the following:

On the main menu, click:

Insert > New Comment

Right-click the stream background and click New Comment on the pop-up menu.

Click the New Comment button in the toolbar.

E Enter the comment text.
E Click another node in the stream to save the comment.

Alternatively, you can:
E Insert a freestanding comment (see previous section).
E Do one of the following:

Select the comment, press F2, then select the node or nugget.

Select the node or nugget, press F2, then select the comment.

(Three-button mice only) Move the mouse pointer over the comment, hold down the middle
button, drag the mouse pointer over the node or nugget, and release the mouse button.

To attach a comment to an additional node or nugget

If a comment is already attached to a node or nugget, or if it is currently at stream level, and you
want to attach it to an additional node or nugget, do one of the following:

Select the comment, press F2, then select the node or nugget.

Select the node or nugget, press F2, then select the comment.

(Three-button mice only) Move the mouse pointer over the comment, hold down the middle
button, drag the mouse pointer over the node or nugget, and release the mouse button.

To edit an existing comment
E Do one of the following:

Double-click the comment text box.

Select the text box and press Enter.

Right-click the text box to display its menu, and click Edit.

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E Edit the comment text. You can use standard Windows shortcut keys when editing, for example

Ctrl+C to copy text. Other options during editing are listed in the pop-up menu for the comment.
E Click outside the text box once to display the resizing controls, then again to complete the

comment.
To resize a comment text box
E Select the comment to display the resizing controls.
E Click and drag a control to resize the box.
E Click outside the text box to save the change.

To move an existing comment

If you want to move a comment but not its attached objects (if any), do one of the following:

Move the mouse pointer over the comment, hold down the left mouse button, and drag the
comment to the new position.

Select the comment, hold down the Alt key, and move the comment using the arrow keys.

If you want to move a comment together with any nodes or nuggets to which the comment is
attached:
E Select all the objects you want to move.
E Do one of the following:

Move the mouse pointer over one of the objects, hold down the left mouse button, and drag
the objects to the new position.

Select one of the objects, hold down the Alt key, and move the objects using the arrow keys.

To disconnect a comment from a node or nugget
E Select one or more comments to be disconnected.
E Do one of the following:

Press F3.

Right-click a selected comment and click Disconnect on its menu.

To delete a comment
E Select one or more comments to be deleted.
E Do one of the following:

Press the Delete key.

Right-click a selected comment and click Delete on its menu.

If the comment was attached to a node or nugget, the connection line is deleted as well.

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If the comment was originally a stream or SuperNode annotation that had been converted to a
freestanding comment, the comment is deleted from the canvas but its text is retained on the
Annotations tab for the stream or SuperNode.
To show or hide comments for a stream
E Do one of the following:

On the main menu, click:

View > Comments

Click the Show/hide comments button in the toolbar.

Listing Stream Comments
You can view a list of all the comments that have been made for a particular stream or SuperNode.
On this list, you can

Change the order of comments

Edit the comment text

Change the foreground or background color of a comment

Listing Comments

To list the comments made for a stream, do one of the following:

On the main menu, click:

Tools > Stream Properties > Comments

Right-click a stream in the managers pane and click Stream Properties, then Comments.

Right-click a stream background on the canvas and click Stream Properties, then Comments.

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Figure 5-39
Listing comments for a stream

Text. The text of the comment. Double-click the text to change the field to an editable text box.
Links. The name of the node to which the comment is attached. If this field is empty, the comment
applies to the stream.
Positioning buttons. These move a selected comment up or down in the list.
Comment Colors. To change the foreground or background color of a comment, select the comment,
select the Custom colors check box, then select a color from the Background or Foreground list (or
both). Click Apply, then click the stream background, to see the effect of the change. Click OK
to save the change.

Converting Annotations to Comments
Annotations made to streams or SuperNodes can be converted into comments.
In the case of streams, the annotation is converted to a freestanding comment (that is, it is not
attached to any nodes) on the stream canvas.
When a SuperNode annotation is converted to a comment, the comment is not attached to the
SuperNode on the stream canvas, but is visible when you zoom in to the SuperNode.
To convert a stream annotation to a comment
E Click Stream Properties on the Tools menu. (Alternatively, you can right-click a stream in the
managers pane and click Stream Properties.)

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E Click the Annotations tab.
E Select the Show annotation as comment check box.
E Click OK.

To convert a SuperNode annotation to a comment
E Double-click the SuperNode icon on the canvas.
E Click the Annotations tab.
E Select the Show annotation as comment check box.
E Click OK.

Annotations
Nodes, streams, and models can be annotated in a number of ways. You can add descriptive
annotations and specify a custom name. These options are useful especially when generating
reports for streams added to the project pane. For nodes and model nuggets, you can also add
ToolTip text to help distinguish between similar nodes on the stream canvas.
Adding Annotations

Editing a node or model nugget opens a tabbed dialog box containing an Annotations tab used to
set a variety of annotation options. You can also open the Annotations tab directly.
E To annotate a node or nugget, right-click the node or nugget on the stream canvas and click
Rename and Annotate. The editing dialog box opens with the Annotations tab visible.
E To annotate a stream, click Stream Properties on the Tools menu. (Alternatively, you can right-click
a stream in the managers pane and click Stream Properties.) Click the Annotations tab.

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Figure 5-40
Annotations tab options

Name. Select Custom to adjust the autogenerated name or to create a unique name for the node
as displayed on the stream canvas.
Tooltip text. (For nodes and model nuggets only) Enter text used as a tooltip on the stream canvas.
This is particularly useful when working with a large number of similar nodes.
Keywords. Specify keywords to be used in project reports and when searching for nodes in a
stream, or tracking objects stored in the repository (see About the IBM SPSS Collaboration
and Deployment Services Repository on p. 158). Multiple keywords can be separated by
semicolons—for example, income; crop type; claim value. White spaces at the beginning and end
of each keyword are trimmed—for example, income ; crop type will produce the same results as
income;crop type. (White spaces within keywords are not trimmed, however. For example, crop
type with one space and crop type with two spaces are not the same.)
The main text area can be used to enter lengthy annotations regarding the operations of the
node or decisions made in the node. For example, when you are sharing and reusing streams, it is
helpful to take notes on decisions such as discarding a field with numerous blanks using a Filter
node. Annotating the node stores this information with the node. You can also choose to include
these annotations in a project report created from the project pane. For more information, see the
topic Introduction to Projects in Chapter 11 on p. 200.

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Show annotation as comment. (For stream and SuperNode annotations only) Check this box
to convert the annotation to a freestanding comment that will be visible on the stream canvas.
For more information, see the topic Adding Comments and Annotations to Nodes and Streams
on p. 78.
ID. Displays a unique ID that can be used to reference the node for the purpose of scripting or
automation. This value is automatically generated when the node is created and will not change.
Also note that to avoid confusion with the letter “O”, zeros are not used in node IDs. Use the copy
button at the right to copy and paste the ID into scripts or elsewhere as needed.

Saving Data Streams
After you have created a stream, you can save it for future reuse.
To Save a Stream
E On the File menu, click Save Stream or Save Stream As.
E In the Save dialog box, browse to the folder in which you want to save the stream file.
E Enter a name for the stream in the File Name text box.
E Select Add to project if you would like to add the saved stream to the current project.

Clicking Save stores the stream with the extension *.str in the specified directory.
Automatic backup files. Each time a stream is saved, the previously saved version of the file
is automatically preserved as a backup, with a hyphen appended to the filename (for example
mystream.str-). To restore the backed-up version, simply delete the hyphen and reopen the file.

Saving States
In addition to streams, you can save states, which include the currently displayed stream diagram
and any model nuggets that you have created (listed on the Models tab in the managers pane).
To Save a State
E On the File menu, click:
State > Save State or Save State As
E In the Save dialog box, browse to the folder in which you want to save the state file.

Clicking Save stores the state with the extension *.cst in the specified directory.

Saving Nodes
You can also save an individual node by right-clicking the node on the stream canvas and clicking
Save Node on the pop-up menu. Use the file extension *.nod.

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Saving Multiple Stream Objects
When you exit IBM® SPSS® Modeler with multiple unsaved objects, such as streams, projects,
or model nuggets, you will be prompted to save before completely closing the software. If you
choose to save items, a dialog box will open with options for saving each object.
Figure 5-41
Saving multiple objects

E Simply select the check boxes for the objects that you want to save.
E Click OK to save each object in the required location.

You will then be prompted with a standard Save dialog box for each object. After you have
finished saving, the application will close as originally instructed.

Saving Output
Tables, graphs, and reports generated from IBM® SPSS® Modeler output nodes can be saved in
output object (*.cou) format.
E When viewing the output you want to save, on the output window menus click:
File > Save
E Specify a name and location for the output file.
E Optionally, select Add file to project in the Save dialog box to include the file in the current project.

For more information, see the topic Introduction to Projects in Chapter 11 on p. 200.
Alternatively, you can right-click any output object listed in the managers pane and select Save
from the pop-up menu.

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Encrypting and Decrypting Information
When you save a stream, node, project, output file, or model nugget, you can encrypt it to prevent
its unauthorized use. To do this, you select an extra option when saving, and add a password to the
item being saved. This encryption can be set for any of the items that you save and adds extra
security to them; it is not the same as the SSL encryption used if you are passing files between
IBM® SPSS® Modeler and IBM® SPSS® Modeler Server.
When you try to open an encrypted item, you are prompted to enter the password. After you
enter the correct password, the item is decrypted automatically and opens as usual.
To Encrypt an Item
E In the Save dialog box, for the item to be encrypted, click Options. The Encryption Options

dialog box opens.
Figure 5-42
Encryption options when saving a file

E Select Encrypt this file.
E Optionally, for further security, select Mask password. This displays anything you enter as a

series of dots.
E Enter the password. Warning: If you forget the password, the file or model cannot be opened.
E If you selected Mask password, re-enter the password to confirm that you entered it correctly.
E Click OK to return to the Save dialog box.

Note: If you save a copy of any encryption-protected item, the new item is automatically saved in
an encrypted format using the original password unless you change the settings in the Encryption
Options dialog box.

Loading Files
You can reload a number of saved objects in IBM® SPSS® Modeler:

Streams (.str)

States (.cst)

Models (.gm)

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Models palette (.gen)

Nodes (.nod)

Output (.cou)

Projects (.cpj)

Opening New Files

Streams can be loaded directly from the File menu.
E On the File menu, click Open Stream.

All other file types can be opened using the submenu items available on the File menu. For
example, to load a model, on the File menu click:
Models > Open Model or Load Models Palette

Opening Recently Used Files

For quick loading of recently used files, you can use the options at the bottom of the File menu.
Figure 5-43
Opening recently used options from the File menu

Select Recent Streams, Recent Projects, or Recent States to expand a list of recently used files.

Mapping Data Streams
Using the mapping tool, you can connect a new data source to a preexisting stream. The mapping
tool will not only set up the connection but it will also help you to specify how fields in the new
source will replace those in the existing stream. Instead of re-creating an entire data stream for a
new data source, you can simply connect to an existing stream.
The data mapping tool allows you to join together two stream fragments and be sure that all
of the (essential) field names match up properly. In essence, mapping data results simply in the
creation of a new Filter node, which matches up the appropriate fields by renaming them.
There are two equivalent ways to map data:
Select replacement node. This method starts with the node to be replaced. First, you right-click the
node to replace; then, using the Data Mapping > Select Replacement Node option from the pop-up
menu, select the node with which to replace it.

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Map to. This method starts with the node to be introduced to the stream. First, right-click the node
to introduce; then, using the Data Mapping > Map To option from the pop-up menu, select the node
to which it should join. This method is particularly useful for mapping to a terminal node. Note:
You cannot map to Merge or Append nodes. Instead, you should simply connect the stream to the
Merge node in the normal manner.
Figure 5-44
Selecting data mapping options

Data mapping is tightly integrated into stream building. If you try to connect to a node that
already has a connection, you will be offered the option of replacing the connection or mapping to
that node.

Mapping Data to a Template
To replace the data source for a template stream with a new source node bringing your own data
into IBM® SPSS® Modeler, you should use the Select Replacement Node option from the Data
Mapping pop-up menu. This option is available for all nodes except Merge, Aggregate, and all
terminal nodes. Using the data mapping tool to perform this action helps ensure that fields are
matched properly between the existing stream operations and the new data source. The following
steps provide an overview of the data mapping process.
Step 1: Specify essential fields in the original source node. In order for stream operations to run
properly, essential fields should be specified. For more information, see the topic Specifying
Essential Fields on p. 94.
Step 2: Add new data source to the stream canvas. Using one of the source nodes, bring in the
new replacement data.

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Step 3: Replace the template source node. Using the Data Mapping option on the pop-up menu
for the template source node, click Select Replacement Node, then select the source node for
the replacement data.
Figure 5-45
Selecting a replacement source node

Step 4: Check mapped fields. In the dialog box that opens, check that the software is mapping fields
properly from the replacement data source to the stream. Any unmapped essential fields are
displayed in red. These fields are used in stream operations and must be replaced with a similar
field in the new data source in order for downstream operations to function properly. For more
information, see the topic Examining Mapped Fields on p. 95.
After using the dialog box to ensure that all essential fields are properly mapped, the old data
source is disconnected and the new data source is connected to the stream using a Filter node
called Map. This Filter node directs the actual mapping of fields in the stream. An Unmap Filter
node is also included on the stream canvas. The Unmap Filter node can be used to reverse field
name mapping by adding it to the stream. It will undo the mapped fields, but note that you will
have to edit any downstream terminal nodes to reselect the fields and overlays.
Figure 5-46
New data source successfully mapped to the stream

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Mapping between Streams
Similar to connecting nodes, this method of data mapping does not require you to set essential
fields beforehand. With this method, you simply connect from one stream to another using Map to
from the Data Mapping pop-up menu. This type of data mapping is useful for mapping to terminal
nodes and copying and pasting between streams. Note: Using the Map to option, you cannot map
to Merge, Append, and all types of source nodes.
Figure 5-47
Mapping a stream from its Sort node to the Type node of another stream

To Map Data between Streams
E Right-click the node that you want to use for connecting to the new stream.
E On the menu, click:
Data Mapping > Map to
E Use the cursor to select a destination node on the target stream.
E In the dialog box that opens, ensure that fields are properly matched and click OK.

Specifying Essential Fields
When mapping to an existing stream, essential fields will typically be specified by the stream
author. These essential fields indicate whether a particular field is used in downstream operations.
For example, the existing stream may build a model that uses a field called Churn. In this stream,
Churn is an essential field because you could not build the model without it. Likewise, fields used
in manipulation nodes, such as a Derive node, are necessary to derive the new field. Explicitly
setting such fields as essential helps to ensure that the proper fields in the new source node are
mapped to them. If mandatory fields are not mapped, you will receive an error message. If you
decide that certain manipulations or output nodes are unnecessary, you can delete the nodes from
the stream and remove the appropriate fields from the Essential Fields list.

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To Set Essential Fields
E Right-click the source node of the template stream that will be replaced.
E On the menu, click:
Data Mapping > Specify Essential Fields
Figure 5-48
Specifying essential fields

E Using the Field Chooser, you can add or remove fields from the list. To open the Field Chooser,

click the icon to the right of the fields list.

Examining Mapped Fields
Once you have selected the point at which one data stream or data source will be mapped to
another, a dialog box opens for you to select fields for mapping or to ensure that the system default
mapping is correct. If essential fields have been set for the stream or data source and they are
unmatched, these fields are displayed in red. Any unmapped fields from the data source will pass
through the Filter node unaltered, but note that you can map non-essential fields as well.
Figure 5-49
Selecting fields for mapping

Original. Lists all fields in the template or existing stream—all of the fields that are present further
downstream. Fields from the new data source will be mapped to these fields.

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Mapped. Lists the fields selected for mapping to template fields. These are the fields whose names
may have to change to match the original fields used in stream operations. Click in the table cell
for a field to activate a list of available fields.

If you are unsure of which fields to map, it may be useful to examine the source data closely
before mapping. For example, you can use the Types tab in the source node to review a summary
of the source data.

Tips and Shortcuts
Work quickly and easily by familiarizing yourself with the following shortcuts and tips:

Build streams quickly by double-clicking. Simply double-click a node on the palette to add and
connect it to the current stream.

Use key combinations to select downstream nodes. Press Ctrl+Q and Ctrl+W to toggle the
selection of all nodes downstream.

Use shortcut keys to connect and disconnect nodes. When a node is selected in the canvas, press
F2 to begin a connection, press Tab to move to the required node, and press Shift+Spacebar to
complete the connection. Press F3 to disconnect all inputs and outputs to the selected node.

Customize the Nodes Palette tab with your favorite nodes. On the Tools menu, click Manage
Palettes to open a dialog box for adding, removing, or moving the nodes shown on the
Nodes Palette.

Figure 5-50
Palette Manager

Rename nodes and add ToolTips. Each node dialog box includes an Annotations tab on which
you can specify a custom name for nodes on the canvas as well as add ToolTips to help
organize your stream. You can also include lengthy annotations to track progress, save process
details, and denote any business decisions required or achieved.

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Figure 5-51
ToolTip and custom node name

Insert values automatically into a CLEM expression. Using the Expression Builder, accessible
from a variety of dialog boxes (such as those for Derive and Filler nodes), you can
automatically insert field values into a CLEM expression. Click the values button on the
Expression Builder to choose from existing field values.

Figure 5-52
Values button

Browse for files quickly. When browsing for files on an Open dialog box, use the File list
(click the yellow diamond button) to access previously used directories as well as IBM®
SPSS® Modeler default directories. Use the forward and back buttons to scroll through
accessed directories.

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Figure 5-53
Selecting the Demos folder from the list of recently-used directories

Minimize output window clutter. You can close and delete output quickly using the red X button
at the top right corner of all output windows. This enables you to keep only promising or
interesting results on the Outputs tab of the managers pane.

A full range of keyboard shortcuts is available for the software. For more information, see the
topic Keyboard Accessibility in Appendix A on p. 238.
Did you know that you can...

Drag and select a group of nodes on the stream canvas using your mouse.

Copy and paste nodes from one stream to another.

Access Help from every dialog box and output window.

Get Help on CRISP-DM, the Cross-Industry Standard Process for Data Mining. (On the
Help menu, click CRISP-DM Help.)

Chapter

Handling Missing Values

Overview of Missing Values
During the Data Preparation phase of data mining, you will often want to replace missing values
in the data. Missing values are values in the data set that are unknown, uncollected, or incorrectly
entered. Usually, such values are invalid for their fields. For example, the field Sex should contain
the values M and F. If you discover the values Y or Z in the field, you can safely assume that such
values are invalid and should therefore be interpreted as blanks. Likewise, a negative value for the
field Age is meaningless and should also be interpreted as a blank. Frequently, such obviously
wrong values are purposely entered, or fields left blank, during a questionnaire to indicate a
nonresponse. At times, you may want to examine these blanks more closely to determine whether
a nonresponse, such as the refusal to give one’s age, is a factor in predicting a specific outcome.
Some modeling techniques handle missing data better than others. For example, C5.0 and
Apriori cope well with values that are explicitly declared as “missing” in a Type node. Other
modeling techniques have trouble dealing with missing values and experience longer training
times, resulting in less-accurate models.
There are several types of missing values recognized by IBM® SPSS® Modeler:

Null or system-missing values. These are nonstring values that have been left blank in the
database or source file and have not been specifically defined as “missing” in a source or
Type node. System-missing values are displayed as $null$. Note that empty strings are not
considered nulls in SPSS Modeler, although they may be treated as nulls by certain databases.

Empty strings and white space. Empty string values and white space (strings with no visible
characters) are treated as distinct from null values. Empty strings are treated as equivalent to
white space for most purposes. For example, if you select the option to treat white space as
blanks in a source or Type node, this setting applies to empty strings as well.

Blank or user-defined missing values. These are values such as unknown, 99, or –1 that are
explicitly defined in a source node or Type node as missing. Optionally, you can also choose
to treat nulls and white space as blanks, which allows them to be flagged for special treatment
and to be excluded from most calculations. For example, you can use the @BLANK function to
treat these values, along with other types of missing values, as blanks.

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Figure 6-1
Specifying missing values for a continuous variable

Reading in mixed data. Note that when you are reading in fields with numeric storage (either
integer, real, time, timestamp, or date), any non-numeric values are set to null or system missing.
This is because, unlike some applications, does not allow mixed storage types within a field. To
avoid this, any fields with mixed data should be read in as strings by changing the storage type in
the source node or external application as necessary.
Reading empty strings from Oracle. When reading from or writing to an Oracle database, be aware
that, unlike SPSS Modeler and unlike most other databases, Oracle treats and stores empty string
values as equivalent to null values. This means that the same data extracted from an Oracle
database may behave differently than when extracted from a file or another database, and the data
may return different results.

Handling Missing Values
You should decide how to treat missing values in light of your business or domain knowledge. To
ease training time and increase accuracy, you may want to remove blanks from your data set. On
the other hand, the presence of blank values may lead to new business opportunities or additional
insights. In choosing the best technique, you should consider the following aspects of your data:

Size of the data set

Number of fields containing blanks

Amount of missing information

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In general terms, there are two approaches you can follow:

You can exclude fields or records with missing values

You can impute, replace, or coerce missing values using a variety of methods

Both of these approaches can be largely automated using the Data Audit node. For example, you
can generate a Filter node that excludes fields with too many missing values to be useful in
modeling, and generate a Supernode that imputes missing values for any or all of the fields that
remain. This is where the real power of the audit comes in, allowing you not only to assess the
current state of your data, but to take action based on the assessment.

Handling Records with Missing Values
If the majority of missing values is concentrated in a small number of records, you can just
exclude those records. For example, a bank usually keeps detailed and complete records on
its loan customers. If, however, the bank is less restrictive in approving loans for its own staff
members, data gathered for staff loans is likely to have several blank fields. In such a case, there
are two options for handling these missing values:

You can use a Select node to remove the staff records.

If the data set is large, you can discard all records with blanks.

Handling Fields with Missing Values
If the majority of missing values is concentrated in a small number of fields, you can address them
at the field level rather than at the record level. This approach also allows you to experiment with
the relative importance of particular fields before deciding on an approach for handling missing
values. If a field is unimportant in modeling, it probably is not worth keeping, regardless of how
many missing values it has.
For example, a market research company may collect data from a general questionnaire
containing 50 questions. Two of the questions address age and political persuasion, information
that many people are reluctant to give. In this case, Age and Political_persuasion have many
missing values.
Field Measurement Level

In determining which method to use, you should also consider the measurement level of fields
with missing values.
Numeric fields. For numeric field types, such as Continuous, you should always eliminate any
non-numeric values before building a model, because many models will not function if blanks are
included in numeric fields.
Categorical fields. For categorical fields, such as Nominal and Flag, altering missing values is not
necessary but will increase the accuracy of the model. For example, a model that uses the field Sex
will still function with meaningless values, such as Y and Z, but removing all values other than M
and F will increase the accuracy of the model.

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Screening or Removing Fields

To screen out fields with too many missing values, you have several options:

You can use a Data Audit node to filter fields based on quality.

You can use a Feature Selection node to screen out fields with more than a specified percentage
of missing values and to rank fields based on importance relative to a specified target.

Instead of removing the fields, you can use a Type node to set the field role to None. This will
keep the fields in the data set but exclude them from the modeling processes.

Imputing or Filling Missing Values
In cases where there are only a few missing values, it may be useful to insert values to replace
the blanks. You can do this from the Data Audit report, which allows you to specify options for
specific fields as appropriate and then generate a SuperNode that imputes values using a number
of methods. This is the most flexible method, and it also allows you to specify handling for large
numbers of fields in a single node.
The following methods are available for imputing missing values:
Fixed. Substitutes a fixed value (either the field mean, midpoint of the range, or a constant that
you specify).
Random. Substitutes a random value based on a normal or uniform distribution.
Expression. Allows you to specify a custom expression. For example, you could replace values
with a global variable created by the Set Globals node.
Algorithm. Substitutes a value predicted by a model based on the C&RT algorithm. For each field
imputed using this method, there will be a separate C&RT model, along with a Filler node that
replaces blanks and nulls with the value predicted by the model. A Filter node is then used to
remove the prediction fields generated by the model.

Alternatively, to coerce values for specific fields, you can use a Type node to ensure that the
field types cover only legal values and then set the Check column to Coerce for the fields whose
blank values need replacing.

CLEM Functions for Missing Values
There are several functions used to handle missing values. The following functions are often used
in Select and Filler nodes to discard or fill missing values:

count_nulls(LIST)

@BLANK(FIELD)

@NULL(FIELD)

undef

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Handling Missing Values

The @ functions can be used in conjunction with the @FIELD function to identify the presence
of blank or null values in one or more fields. The fields can simply be flagged when blank
or null values are present, or they can be filled with replacement values or used in a variety
of other operations.
You can count nulls across a list of fields, as follows:
count_nulls(['cardtenure' 'card2tenure' 'card3tenure'])

When using any of the functions that accept a list of fields as input, the special functions
@FIELDS_BETWEEN and @FIELDS_MATCHING can be used, as shown in the following example:
count_nulls(@FIELDS_MATCHING('card*'))
Figure 6-2
Using a Filler node to replace blank values with 0 in the selected field

You can use the undef function to fill fields with the system-missing value, displayed as $null$.
For example, to replace any numeric value, you could use a conditional statement, such as:
if not(Age > 17) or not(Age < 66) then undef else Age endif

This replaces anything that is not in the range with a system-missing value, displayed as $null$. By
using the not() function, you can catch all other numeric values, including any negatives. For more
information, see the topic Functions Handling Blanks and Null Values in Chapter 8 on p. 156.

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Note on Discarding Records

When using a Select node to discard records, note that syntax uses three-valued logic and
automatically includes null values in select statements. To exclude null values (system-missing)
in a select expression, you must explicitly specify this by using and not in the expression. For
example, to select and include all records where the type of prescription drug is Drug C, you
would use the following select statement:
Drug = 'drugC' and not(@NULL(Drug))

Earlier versions of excluded null values in such situations.

Chapter

Building CLEM Expressions

About CLEM
The Control Language for Expression Manipulation (CLEM) is a powerful language for analyzing
and manipulating the data that flows along IBM® SPSS® Modeler streams. Data miners use
CLEM extensively in stream operations to perform tasks as simple as deriving profit from cost
and revenue data or as complex as transforming web log data into a set of fields and records with
usable information.
CLEM is used within SPSS Modeler to:

Compare and evaluate conditions on record fields.

Derive values for new fields.

Derive new values for existing fields.

Reason about the sequence of records.

Insert data from records into reports.

Scripting. A subset of the CLEM language can also be used when scripting in the user interface.
This allows you to perform many of the same data manipulations in an automated fashion.

CLEM expressions are indispensable for data preparation in SPSS Modeler and can be used in
a wide range of nodes—from record and field operations (Select, Balance, Filler) to plots and
output (Analysis, Report, Table). For example, you can use CLEM in a Derive node to create a
new field based on a formula such as ratio.

105

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Figure 7-1
Derive node creating a new field based on a formula

CLEM expressions can also be used for global search and replace operations. For example, the
expression @NULL(@FIELD) can be used in a Filler node to replace system-missing values with the
integer value 0. (To replace user-missing values, also called blanks, use the @BLANK function.)

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Building CLEM Expressions
Figure 7-2
Filler node replacing system-missing values with 0

More complex CLEM expressions can also be created. For example, you can derive new fields
based on a conditional set of rules.

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Figure 7-3
Conditional Derive comparing values of one field to those of the field before it

CLEM Examples
To illustrate correct syntax as well as the types of expressions possible with CLEM, example
expressions follow.
Simple Expressions

Formulas can be as simple as this one, which derives a new field based on the values of the
fields After and Before:
(After - Before) / Before * 100.0

Notice that field names are unquoted when referring to the values of the field.
Similarly, the following expression simply returns the log of each value for the field salary.
log(salary)

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Building CLEM Expressions

Complex Expressions

Expressions can also be lengthy and more complex. The following expression returns true if the
value of two fields ($KX-Kohonen and $KY-Kohonen) fall within the specified ranges. Notice that
here the field names are single-quoted because the field names contain special characters.
('$KX-Kohonen' >= -0.2635771036148072 and '$KX-Kohonen' <= 0.3146203637123107
and '$KY-Kohonen' >= -0.18975617885589602 and
'$KY-Kohonen' <= 0.17674794197082522) -> T

Several functions, such as string functions, require you to enter several parameters using correct
syntax. In the following example, the function subscrs is used to return the first character of a
produce_ID field, indicating whether an item is organic, genetically modified, or conventional.
The results of an expression are described by -> `result`.
subscrs(1,produce_ID) -> `c`

Similarly, the following expression is:
stripchar(`3`,`123`) -> `12`

It is important to note that characters are always encapsulated within single backquotes.
Combining Functions in an Expression

Frequently, CLEM expressions consist of a combination of functions. The following function
combines subscr and lowertoupper to return the first character of produce_ID and convert it to
upper case.
lowertoupper(subscr(1,produce_ID)) -> `C`

This same expression can be written in shorthand as:
lowertoupper(produce_ID(1)) -> `C`

Another commonly used combination of functions is:
locchar_back(`n`, (length(web_page)), web_page)

This expression locates the character `n` within the values of the field web_page reading backward
from the last character of the field value. By including the length function as well, the expression
dynamically calculates the length of the current value rather than using a static number, such as 7,
which will be invalid for values with less than seven characters.
Special Functions

Numerous special functions (preceded with an @ symbol) are available. Commonly used
functions include:
@BLANK('referrer ID') -> T

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Frequently, special functions are used in combination, which is a commonly used method of
flagging blanks in more than one field at a time.
@BLANK(@FIELD)-> T

Additional examples are discussed throughout the CLEM documentation. For more information,
see the topic CLEM Reference Overview in Chapter 8 on p. 127.

Values and Data Types
CLEM expressions are similar to formulas constructed from values, field names, operators, and
functions. The simplest valid CLEM expression is a value or a field name. Examples of valid
values are:
3
1.79
'banana'

Examples of field names are:
Product_ID
'$P-NextField'

where Product is the name of a field from a market basket data set, ’$P-NextField’ is the name
of a parameter, and the value of the expression is the value of the named field. Typically, field
names start with a letter and may also contain digits and underscores (_). You can use names that
do not follow these rules if you place the name within quotation marks. CLEM values can be
any of the following:

Strings—for example, "c1", "Type 2", "a piece of free text"

Integers—for example, 12, 0, –189

Real numbers—for example, 12.34, 0.0, –0.0045

Date/time fields—for example, 05/12/2002, 12/05/2002, 12/05/02

It is also possible to use the following elements:

Character codes—for example, `a` or 3

Lists of items—for example, [1 2 3], ['Type 1' 'Type 2']

Character codes and lists do not usually occur as field values. Typically, they are used as
arguments of CLEM functions.
Quoting Rules

Although the software is flexible when determining the fields, values, parameters, and strings
used in a CLEM expression, the following general rules provide a list of “best practices” to use
when creating expressions:

Strings—Always use double quotes when writing strings ("Type 2" or "value"). Single quotes
can be used instead but at the risk of confusion with quoted fields.

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Building CLEM Expressions

Characters—Always use single backquotes like this ` . For example, note the character d in
the function stripchar(`d`,"drugA"). The only exception to this is when you are using an integer
to refer to a specific character in a string. For example, note the character 5 in the function
lowertoupper("druga"(5)) —> "A". Note: On a standard U.K. and U.S. keyboard, the key for the
backquote character (grave accent, Unicode 0060) can be found just below the Esc key.

Fields—Fields are typically unquoted when used in CLEM expressions (subscr(2,arrayID))
—> CHAR). You can use single quotes when necessary to enclose spaces or other special
characters ('Order Number'). Fields that are quoted but undefined in the data set will be
misread as strings.

Parameters—Always use single quotes ('$P-threshold').

Expressions and Conditions
CLEM expressions can return a result (used when deriving new values)—for example:
Weight * 2.2
Age + 1
sqrt(Signal-Echo)

Or, they can evaluate true or false (used when selecting on a condition)—for example:
Drug = "drugA"
Age < 16
not(PowerFlux) and Power > 2000

You can combine operators and functions arbitrarily in CLEM expressions—for example:
sqrt(abs(Signal)) * max(T1, T2) + Baseline

Brackets and operator precedence determine the order in which the expression is evaluated. In this
example, the order of evaluation is:

abs(Signal) is evaluated, and sqrt is applied to its result.

max(T1, T2) is evaluated.

The two results are multiplied: x has higher precedence than +.

Finally, Baseline is added to the result.

The descending order of precedence (that is, operations that are performed first to operations
that are performed last) is as follows:

Function arguments

Function calls

x / mod div rem

+–

> < >= <= /== == = /=

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If you want to override precedence, or if you are in any doubt of the order of evaluation, you can
use parentheses to make it explicit—for example,
sqrt(abs(Signal)) * (max(T1, T2) + Baseline)

Stream, Session, and SuperNode Parameters
Parameters can be defined for use in CLEM expressions and in scripting. They are, in effect,
user-defined variables that are saved and persisted with the current stream, session, or SuperNode
and can be accessed from the user interface as well as through scripting. If you save a stream, for
example, any parameters set for that stream are also saved. (This distinguishes them from local
script variables, which can be used only in the script in which they are declared.) Parameters are
often used in scripting as part of a CLEM expression in which the parameter value is specified in
the script.
The scope of a parameter depends on where it is set:

Parameters can also be set for SuperNodes, in which case they are visible only to nodes
encapsulated within that SuperNode.
Using Parameters in CLEM Expressions

Parameters are represented in CLEM expressions by $P-pname, where pname is the name of
the parameter. When used in CLEM expressions, parameters must be placed within single
quotes––for example, '$P-scale'.
Available parameters are easily viewed using the Expression Builder. To view current parameters:
E In any dialog box accepting CLEM expressions, click the Expression Builder button.
E From the Fields list, select Parameters.

You can select parameters from the list for insertion into the CLEM expression. For more
information, see the topic Selecting Fields, Parameters, and Global Variables on p. 121.

Working with Strings
There are a number of operations available for strings, including:

Converting a string to upper case or lower case—uppertolower(CHAR).

Removing specified characters, such as `ID_` or `$`, from a string
variable—stripchar(CHAR,STRING).

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Building CLEM Expressions

Determining the length (number of characters) for a string variable—length(STRING).

Checking the alphabetical ordering of string values—alphabefore(STRING1, STRING2).

Removing leading or trailing white space from values—trim(STRING), trim_start(STRING),
or trimend(STRING).

Extract the first or last n characters from a string—startstring(LENGTH, STRING) or
endstring(LENGTH, STRING). For example, suppose you have a field named item that combines
a product name with a four-digit ID code (ACME CAMERA-D109). To create a new field that
contains only the four-digit code, specify the following formula in a Derive node:

endstring(4, item)

Matching a specific pattern—STRING matches PATTERN. For example, to select persons with
“market” anywhere in their job title, you could specify the following in a Select node:

job_title matches "*market*"

Replacing all instances of a substring within a string—replace(SUBSTRING, NEWSUBSTRING,
STRING). For example, to replace all instances of an unsupported character, such as a vertical
pipe ( | ), with a semicolon prior to text mining, use the replace function in a Filler node.
Under Fill in fields:, select all fields where the character may occur. For the Replace: condition,
select Always, and specify the following condition under Replace with:

replace('|',';',@FIELD)

Deriving a flag field based on the presence of a specific substring. For example, you could
use a string function in a Derive node to generate a separate flag field for each response
with an expression such as:

hassubstring(museums,"museum_of_design")

For more information, see the topic String Functions in Chapter 8 on p. 141.

Handling Blanks and Missing Values
Replacing blanks or missing values is a common data preparation task for data miners. CLEM
provides you with a number of tools to automate blank handling. The Filler node is the most
common place to work with blanks; however, the following functions can be used in any node that
accepts CLEM expressions:

@BLANK(FIELD) can be used to determine records whose values are blank for a particular
field, such as Age.

@NULL(FIELD) can be used to determine records whose values are system-missing for the
specified field(s). In IBM® SPSS® Modeler, system-missing values are displayed as $null$
values.

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Figure 7-4
Filler node replacing system-missing values with 0

For more information, see the topic Functions Handling Blanks and Null Values in Chapter 8
on p. 156.

Working with Numbers
Numerous standard operations on numeric values are available in IBM® SPSS® Modeler, such as:

Calculating the sine of the specified angle—sin(NUM)

Calculating the natural log of numeric fields—log(NUM)

Calculating the sum of two numbers—NUM1 + NUM2

For more information, see the topic Numeric Functions in Chapter 8 on p. 138.

Working with Times and Dates
Time and date formats may vary depending on your data source and locale. The formats of date
and time are specific to each stream and are set in the stream properties dialog box. The following
examples are commonly used functions for working with date/time fields.

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Calculating Time Passed

You can easily calculate the time passed from a baseline date using a family of functions similar to
the following one. This function returns the time in months from the baseline date to the date
represented by the date string DATE as a real number. This is an approximate figure, based on a
month of 30.0 days.
date_in_months(Date)

Comparing Date/Time Values

Values of date/time fields can be compared across records using functions similar to the following
one. This function returns a value of true if the date string DATE1 represents a date prior to that
represented by the date string DATE2. Otherwise, this function returns a value of 0.
date_before(Date1, Date2)

Calculating Differences

You can also calculate the difference between two times and two dates using functions, such as:
date_weeks_difference(Date1, Date2)

This function returns the time in weeks from the date represented by the date string DATE1 to the
date represented by the date string DATE2 as a real number. This is based on a week of 7.0 days. If
DATE2 is prior to DATE1, this function returns a negative number.
Today’s Date

The current date can be added to the data set using the function @TODAY. Today’s date is added as
a string to the specified field or new field using the date format selected in the stream properties
dialog box. For more information, see the topic Date and Time Functions in Chapter 8 on p. 146.

Summarizing Multiple Fields
The CLEM language includes a number of functions that return summary statistics across
multiple fields. These functions may be particularly useful in analyzing survey data, where
multiple responses to a question may be stored in multiple fields. For more information, see the
topic Working with Multiple-Response Data on p. 117.
Comparison Functions

You can compare values across multiple fields using the min_n and max_n functions—for example:
max_n(['card1fee' 'card2fee''card3fee''card4fee'])

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You can also use a number of counting functions to obtain counts of values that meet specific
criteria, even when those values are stored in multiple fields. For example, to count the number of
cards that have been held for more than five years:
count_greater_than(5, ['cardtenure' 'card2tenure' 'card3tenure'])

To count null values across the same set of fields:
count_nulls(['cardtenure' 'card2tenure' 'card3tenure'])

Note that this example counts the number of cards being held, not the number of people holding
them. For more information, see the topic Comparison Functions in Chapter 8 on p. 135.
To count the number of times a specified value occurs across multiple fields, you can use the
count_equal function. The following example counts the number of fields in the list that contain
the value Y.
count_equal("Y",[Answer1, Answer2, Answer3])

Given the following values for the fields in the list, the function returns the results for the value Y
as shown.
Answer1
Y
Y

Answer2
N
N

Answer3
Y
N

Count
2
1

Numeric Functions

You can obtain statistics across multiple fields using the sum_n, mean_n, and sdev_n
functions—for example:
sum_n(['card1bal' 'card2bal''card3bal'])
mean_n(['card1bal' 'card2bal''card3bal'])

For more information, see the topic Numeric Functions in Chapter 8 on p. 138.
Generating Lists of Fields

When using any of the functions that accept a list of fields as input, the special functions
@FIELDS_BETWEEN(start, end) and @FIELDS_MATCHING(pattern) can be used as input. For
example, assuming the order of fields is as shown in the sum_n example earlier, the following
would be equivalent:
sum_n(@FIELDS_BETWEEN(card1bal, card3bal))

Alternatively, to count the number of null values across all fields beginning with “card”:
count_nulls(@FIELDS_MATCHING('card*'))

For more information, see the topic Special Fields in Chapter 8 on p. 157.

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Working with Multiple-Response Data
A number of comparison functions can be used to analyze multiple-response data, including:

value_at

first_index / last_index

first_non_null / last_non_null

first_non_null_index / last_non_null_index

min_index / max_index

For example, suppose a multiple-response question asked for the first, second, and third
most important reasons for deciding on a particular purchase (for example, price, personal
recommendation, review, local supplier, other). In this case, you might determine the importance
of price by deriving the index of the field in which it was first included:
first_index("price", [Reason1 Reason2 Reason3])

Similarly, suppose you have asked customers to rank three cars in order of likelihood to purchase
and coded the responses in three separate fields, as follows:
customer id
101
102
103

car1
1
3
2

car2
3
2
3

car3
2
1
1

In this case, you could determine the index of the field for the car they like most (ranked #1, or
the lowest rank) using the min_index function:
min_index(['car1' 'car2' 'car3'])

For more information, see the topic Comparison Functions in Chapter 8 on p. 135.
Referencing Multiple-Response Sets

The special @MULTI_RESPONSE_SET function can be used to reference all of the fields in a
multiple-response set. For example, if the three car fields in the previous example are included in
a multiple-response set named car_rankings, the following would return the same result:
max_index(@MULTI_RESPONSE_SET("car_rankings"))

The Expression Builder
You can type CLEM expressions manually or use the Expression Builder, which displays a
complete list of CLEM functions and operators as well as data fields from the current stream,
allowing you to quickly build expressions without memorizing the exact names of fields or

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functions. In addition, the Builder controls automatically add the proper quotes for fields and
values, making it easier to create syntactically correct expressions.
Figure 7-5
Expression Builder dialog box

Note: The Expression Builder is not supported in scripting or parameter settings.

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Accessing the Expression Builder
The Expression Builder is available in all nodes where CLEM expressions are used, including
Select, Balance, Derive, Filler, Analysis, Report, and Table nodes. You can open it by clicking the
calculator button just to the right of the formula field.
Figure 7-6
A variety of nodes with Expression Builder button

Creating Expressions
The Expression Builder provides not only complete lists of fields, functions, and operators but
also access to data values if your data is instantiated.
To Create an Expression Using the Expression Builder
E Type in the expression field, using the function and field lists as references.

or
E Select the required fields and functions from the scrolling lists.

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E Double-click or click the yellow arrow button to add the field or function to the expression field.
E Use the operand buttons in the center of the dialog box to insert the operations into the expression.

Selecting Functions
The function list displays all available CLEM functions and operators. Scroll to select a function
from the list, or, for easier searching, use the drop-down list to display a subset of functions or
operators. Available functions are grouped into categories for easier searching.
Figure 7-7
Functions drop-down list

Most of these categories are described in the Reference section of the CLEM language description.
For more information, see the topic Functions Reference in Chapter 8 on p. 133.
The other categories are as follows.

General Functions contains a selection of some of the most commonly-used functions.

Recently Used contains a list of CLEM functions used within the current session.

@ Functions contains a list of all the special functions, which have their names preceded
by an “@” sign.

Database Functions. If the stream includes a database connection (by means of a Database
source node), this selection lists the functions available from within that database, including
user-defined functions (UDFs).

Operators lists all the operators you can use when building expressions. Operators are also
available from the buttons in the center of the dialog box.

All Functions contains a complete list of available CLEM functions.

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After you have selected a group of functions, double-click to insert the functions into the
expression field at the point indicated by the position of the cursor.

Selecting Fields, Parameters, and Global Variables
The field list displays all fields available at this point in the data stream. Scroll to select a field
from the list. Double-click or click the yellow arrow button to add a field to the expression.
Figure 7-8
Expression Builder: Fields list

For more information, see the topic Stream, Session, and SuperNode Parameters on p. 112.
In addition to fields, you can also choose from the following items:
Multiple-response sets. For more information, see the IBM SPSS Modeler Source, Process, and
Output Nodes guide.
Recently used contains a list of fields, multiple-response sets, parameters, and global values used
within the current session.
Parameters. For more information, see the topic Stream, Session, and SuperNode Parameters
on p. 112.
Global values. For more information, see the IBM SPSS Modeler Source, Process, and Output
Nodes guide.

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Viewing or Selecting Values
Field values can be viewed from a number of places in the system, including the Expression
Builder, data audit reports, and when editing future values in a Time Intervals node. Note that
data must be fully instantiated in a source or Type node to use this feature, so that storage, types,
and values are known.
Figure 7-9
Fields list with values shown for selected field

E To view values for a field from the Expression Builder or a Time Intervals node, select the required

field and click the value picker button to open a dialog box listing values for the selected field.
You can then select a value and click Insert to paste the value into the current expression or list.
Figure 7-10
Value picker button

For flag and nominal fields, all defined values are listed. For continuous (numeric range) fields,
the minimum and maximum values are displayed.

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Checking CLEM Expressions
Click Check in the Expression Builder (lower right corner) to validate the expression. Expressions
that have not been checked are displayed in red. If errors are found, a message indicating the
cause is displayed.
Figure 7-11
Invalid CLEM expression

The following items are checked:

Correct quoting of values and field names

Correct usage of parameters and global variables

Valid usage of operators

Existence of referenced fields

Existence and definition of referenced globals

If you encounter errors in syntax, try creating the expression using the lists and operator buttons
rather than typing the expression manually. This method automatically adds the proper quotes
for fields and values.

Find and Replace
The Find/Replace dialog box is available in places where you edit script or expression text,
including the script editor, CLEM expression builder, or when defining a template in the Report
node. When editing text in any of these areas, press Ctrl-F to access the dialog box, making sure
cursor has focus in a text area. If working in a Filler node, for example, you can access the dialog
box from any of the text areas on the Settings tab, or from the text field in the Expression Builder.

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Figure 7-12
Find/Replace dialog box

E With the cursor in a text area, press Ctrl+F to access the Find/Replace dialog box.
E Enter the text you want to search for, or choose from the drop-down list of recently searched items.
E Enter the replacement text, if any.
E Click Find Next to start the search.
E Click Replace to replace the current selection, or Replace All to update all or selected instances.
E The dialog box closes after each operation. Press F3 from any text area to repeat the last find

operation, or press Ctrl+F to access the dialog box again.
Search Options
Match case. Specifies whether the find operation is case-sensitive; for example, whether myvar
matches myVar. Replacement text is always inserted exactly as entered, regardless of this setting.
Whole words only. Specifies whether the find operation matches text embedded within words. If
selected, for example, a search on spider will not match spiderman or spider-man.
Regular expressions. Specifies whether regular expression syntax is used (see next section). When
selected, the Whole words only option is disabled and its value is ignored.
Selected text only. Controls the scope of the search when using the Replace All option.
Regular Expression Syntax

Regular expressions allow you to search on special characters such as tabs or newline characters,
classes or ranges of characters such as a through d, any digit or non-digit, and boundaries such as
the beginning or end of a line. The following types of expressions are supported.
Character Matches
Characters
x
\\
\0n

Matches
The character x
The backslash character
The character with octal value 0n (0 <= n <= 7)

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Characters
\0nn
\0mnn
\xhh
\uhhhh
\t
\n
\r
\f
\a
\e
\cx

Matches
The character with octal value 0nn (0 <= n <= 7)
The character with octal value 0mnn (0 <= m <= 3, 0 <= n <= 7)
The character with hexadecimal value 0xhh
The character with hexadecimal value 0xhhhh
The tab character (‘\u0009’)
The newline (line feed) character (‘\u000A’)
The carriage-return character (‘\u000D’)
The form-feed character (‘\u000C’)
The alert (bell) character (‘\u0007’)
The escape character (‘\u001B’)
The control character corresponding to x

Matching Character Classes
Character classes
[abc]
[^abc]
[a-zA-Z]
[a-d[m-p]]
[a-z&&[def]]
[a-z&&[^bc]]
[a-z&&[^m-p]]

Matches
a, b, or c (simple class)
Any character except a, b, or c (subtraction)
a through z or A through Z, inclusive (range)
a through d, or m through p (union). Alternatively this could be specified
as [a-dm-p]
a through z, and d, e, or f (intersection)
a through z, except for b and c (subtraction). Alternatively this could
be specified as [ad-z]
a through z, and not m through p (subtraction). Alternatively this could
be specified as [a-lq-z]

Predefined Character Classes
Predefined character classes
.
\d
\D
\s
\S
\w
\W

Matches
Any character (may or may not match line terminators)
Any digit: [0-9]
A non-digit: [^0-9]
A white space character: [ \t\n\x0B\f\r]
A non-white space character: [^\s]
A word character: [a-zA-Z_0-9]
A non-word character: [^\w]

Boundary Matches
Boundary matchers
^
$
\b
\B
\A

Matches
The beginning of a line
The end of a line
A word boundary
A non-word boundary
The beginning of the input

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Boundary matchers
\Z
\z

Matches
The end of the input but for the final terminator, if any
The end of the input

Chapter

CLEM Language Reference

CLEM Reference Overview
This section describes the Control Language for Expression Manipulation (CLEM), which is a
powerful tool used to analyze and manipulate the data used in IBM® SPSS® Modeler streams.
You can use CLEM within nodes to perform tasks ranging from evaluating conditions or deriving
values to inserting data into reports. For more information, see the topic About CLEM in
Chapter 7 on p. 105.
A subset of the CLEM language can also be used when you are scripting in the user interface.
This allows you to perform many of the same data manipulations in an automated fashion.
CLEM expressions consist of values, field names, operators, and functions. Using the correct
syntax, you can create a wide variety of powerful data operations. For more information, see the
topic CLEM Examples in Chapter 7 on p. 108.

CLEM Datatypes
CLEM datatypes can be made up of any of the following:

Integers

Reals

Characters

Strings

Lists

Fields

Date/Time

Rules for Quoting

Although IBM® SPSS® Modeler is flexible when you are determining the fields, values,
parameters, and strings used in a CLEM expression, the following general rules provide a list of
“good practices” to use in creating expressions:

Strings—Always use double quotes when writing strings, such as "Type 2". Single quotes can
be used instead but at the risk of confusion with quoted fields.

Fields—Use single quotes only where necessary to enclose spaces or other special characters,
such as 'Order Number'. Fields that are quoted but undefined in the data set will be misread
as strings.

Parameters—Always use single quotes when using parameters, such as '$P-threshold'.

Characters—Always use single backquotes (`), such as stripchar(`d`, "drugA").

127

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For more information, see the topic Values and Data Types in Chapter 7 on p. 110. Additionally,
these rules are covered in more detail in the following topics.

Integers
Integers are represented as a sequence of decimal digits. Optionally, you can place a minus sign
(−) before the integer to denote a negative number—for example, 1234, 999, −77.
The CLEM language handles integers of arbitrary precision. The maximum integer size
depends on your platform. If the values are too large to be displayed in an integer field, changing
the field type to Real usually restores the value.

Reals
Real refers to a floating-point number. Reals are represented by one or more digits followed by a
decimal point followed by one or more digits. CLEM reals are held in double precision.
Optionally, you can place a minus sign (−) before the real to denote a negative number––for
example, 1.234, 0.999, −77.001. Use the form e to express a real number
in exponential notation—for example, 1234.0e5, 1.7e−2. When the IBM® SPSS® Modeler
application reads number strings from files and converts them automatically to numbers, numbers
with no leading digit before the decimal point or with no digit after the point are accepted––for
example, 999. or .11. However, these forms are illegal in CLEM expressions.
Note: When referencing real numbers in CLEM expressions, a period must be used as the decimal
separator, regardless of any settings for the current stream or locale. For example, specify
Na > 0.6

rather than
Na > 0,6

This applies even if a comma is selected as the decimal symbol in the stream properties dialog
box and is consistent with the general guideline that code syntax should be independent of any
specific locale or convention.

Characters
Characters (usually shown as CHAR) are typically used within a CLEM expression to perform
tests on strings. For example, you can use the function isuppercode to determine whether the first
character of a string is upper case. The following CLEM expression uses a character to indicate
that the test should be performed on the first character of the string:
isuppercode(subscrs(1, "MyString"))

To express the code (in contrast to the location) of a particular character in a CLEM expression,
use single backquotes of the form ``––for example, `A`, `Z`.
Note: There is no CHAR storage type for a field, so if a field is derived or filled with an expression
that results in a CHAR, then that result will be converted to a string.

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Strings
Generally, you should enclose strings in double quotation marks. Examples of strings are
"c35product2" and "referrerID". To indicate special characters in a string, use a backslash––for
example, "\$65443". (To indicate a backslash character, use a double backslash, \\.) You can use
single quotes around a string, but the result is indistinguishable from a quoted field ('referrerID').
For more information, see the topic String Functions on p. 141.

Lists
A list is an ordered sequence of elements, which may be of mixed type. Lists are enclosed in
square brackets ([]). Examples of lists are [1 2 4 16] and ["abc" "def"]. Lists are not used as the value
of IBM® SPSS® Modeler fields. They are used to provide arguments to functions, such as
member and oneof.

Fields
Names in CLEM expressions that are not names of functions are assumed to be field names. You
can write these simply as Power, val27, state_flag, and so on, but if the name begins with a digit
or includes non-alphabetic characters, such as spaces (with the exception of the underscore), place
the name within single quotation marks––for example, 'Power Increase', '2nd answer', '#101',
'$P-NextField'.
Note: Fields that are quoted but undefined in the data set will be misread as strings.

Dates
Date calculations are based on a “baseline” date, which is specified in the stream properties dialog
box. The default baseline date is 1 January 1900. For more information, see the topic Setting
general options for streams in Chapter 5 on p. 55.
The CLEM language supports the following date formats.
Format
DDMMYY
MMDDYY
YYMMDD
YYYYMMDD
YYYYDDD

DAY

MONTH
DD/MM/YY

Examples
150163
011563
630115
19630115
Four-digit year followed by a three-digit
number representing the day of the
year—for example, 2000032 represents
the 32nd day of 2000, or 1 February 2000.
Day of the week in the current
locale—for example, Monday, Tuesday,
..., in English.
Month in the current locale—for
example, January, February, ….
15/01/63

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Format
DD/MM/YYYY
MM/DD/YY
MM/DD/YYYY
DD-MM-YY
DD-MM-YYYY
MM-DD-YY
MM-DD-YYYY
DD.MM.YY
DD.MM.YYYY
MM.DD.YY
MM.DD.YYYY
DD-MON-YY
DD/MON/YY
DD.MON.YY
DD-MON-YYYY
DD/MON/YYYY
DD.MON.YYYY
MON YYYY
q Q YYYY

ww WK YYYY

Examples
15/01/1963
01/15/63
01/15/1963
15-01-63
15-01-1963
01-15-63
01-15-1963
15.01.63
15.01.1963
01.15.63
01.15.1963
15-JAN-63, 15-jan-63, 15-Jan-63
15/JAN/63, 15/jan/63, 15/Jan/63
15.JAN.63, 15.jan.63, 15.Jan.63
15-JAN-1963, 15-jan-1963, 15-Jan-1963
15/JAN/1963, 15/jan/1963, 15/Jan/1963
15.JAN.1963, 15.jan.1963, 15.Jan.1963
Jan 2004
Date represented as a digit (1–4)
representing the quarter followed by
the letter Q and a four-digit year—for
example, 25 December 2004 would be
represented as 4 Q 2004.
Two-digit number representing the week
of the year followed by the letters WK
and then a four-digit year. The week of
the year is calculated assuming that the
first day of the week is Monday and there
is at least one day in the first week.

Time
The CLEM language supports the following time formats.
Format
HHMMSS
HHMM
MMSS
HH:MM:SS
HH:MM
MM:SS
(H)H:(M)M:(S)S
(H)H:(M)M
(M)M:(S)S
HH.MM.SS
HH.MM

Examples
120112, 010101, 221212
1223, 0745, 2207
5558, 0100
12:01:12, 01:01:01, 22:12:12
12:23, 07:45, 22:07
55:58, 01:00
12:1:12, 1:1:1, 22:12:12
12:23, 7:45, 22:7
55:58, 1:0
12.01.12, 01.01.01, 22.12.12
12.23, 07.45, 22.07

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Format
MM.SS
(H)H.(M)M.(S)S
(H)H.(M)M
(M)M.(S)S

Examples
55.58, 01.00
12.1.12, 1.1.1, 22.12.12
12.23, 7.45, 22.7
55.58, 1.0

CLEM Operators
The following operators are available.
Operation
or

and
=
==
/=
/==
>
>=
<
<=

&&=_0
&&/=_0
+
><
-

Comments
Used between two CLEM expressions.
Returns a value of true if either is true
or if both are true.
Used between two CLEM expressions.
Returns a value of true if both are true.
Used between any two comparable
items. Returns true if ITEM1 is equal
to ITEM2.
Identical to =.
Used between any two comparable
items. Returns true if ITEM1 is not
equal to ITEM2.
Identical to /=.
Used between any two comparable
items. Returns true if ITEM1 is strictly
greater than ITEM2.
Used between any two comparable
items. Returns true if ITEM1 is greater
than or equal to ITEM2.
Used between any two comparable
items. Returns true if ITEM1 is strictly
less than ITEM2
Used between any two comparable
items. Returns true if ITEM1 is less
than or equal to ITEM2.
Used between two integers. Equivalent
to the Boolean expression INT1 &&
INT2 = 0.
Used between two integers. Equivalent
to the Boolean expression INT1 &&
INT2 /= 0.
Adds two numbers: NUM1 + NUM2.
Concatenates two strings; for example,
STRING1 >< STRING2.
Subtracts one number from another:
NUM1 - NUM2. Can also be used in
front of a number: - NUM.
Used to multiply two numbers: NUM1
* NUM2.

Precedence (see next section)
10

9
7

7
7

7
6

5
5
5

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Operation
&&
&&~~

||
~~
||/&
INT1 << N
INT1 >> N
/
**
rem

div

Comments
Used between two integers. The result
is the bitwise ‘and’ of the integers INT1
and INT2.
Used between two integers. The result
is the bitwise ‘and’ of INT1 and the
bitwise complement of INT2.
Used between two integers. The result
is the bitwise ‘inclusive or’ of INT1
and INT2.
Used in front of an integer. Produces
the bitwise complement of INT.
Used between two integers. The result
is the bitwise ‘exclusive or’ of INT1
and INT2.
Used between two integers. Produces
the bit pattern of INT shifted left by N
positions.
Used between two integers. Produces
the bit pattern of INT shifted right by
N positions.
Used to divide one number by another:
NUM1 / NUM2.
Used between two numbers: BASE **
POWER. Returns BASE raised to the
power POWER.
Used between two integers: INT1 rem
INT2. Returns the remainder, INT1 (INT1 div INT2) * INT2.
Used between two integers: INT1 div
INT2. Performs integer division.

Precedence (see next section)
4

4
4

4
3

Operator Precedence

Precedences determine the parsing of complex expressions, especially unbracketed expressions
with more than one infix operator. For example,
3+4*5

parses as 3 + (4 * 5) rather than (3 + 4) * 5 because the relative precedences dictate that * is to be
parsed before +. Every operator in the CLEM language has a precedence value associated with it;
the lower this value, the more important it is on the parsing list, meaning that it will be processed
sooner than other operators with higher precedence values.

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Functions Reference
The following CLEM functions are available for working with data in IBM® SPSS® Modeler.
You can enter these functions as code in a variety of dialog boxes, such as Derive and Set To
Flag nodes, or you can use the Expression Builder to create valid CLEM expressions without
memorizing function lists or field names.
Function Type
Information
Conversion
Comparison
Logical
Numeric
Trigonometric
Probability
Bitwise
Random
String
SoundEx
Date and time
Sequence
Global

Blanks and null
Special fields

Description
Used to gain insight into field values. For example, the function
is_string returns true for all records whose type is a string.
Used to construct new fields or convert storage type. For example,
the function to_timestamp converts the selected field to a timestamp.
Used to compare field values to each other or to a specified string.
For example, <= is used to compare whether the values of two fields
are lesser or equal.
Used to perform logical operations, such as if, then, else operations.
Used to perform numeric calculations, such as the natural log of
field values.
Used to perform trigonometric calculations, such as the arccosine
of a specified angle.
Return probabilities based on various distributions, such as
probability that a value from Student’s t distribution will be less
than a specific value.
Used to manipulate integers as bit patterns.
Used to randomly select items or generate numbers.
Used to perform a wide variety of operations on strings, such as
stripchar, which allows you to remove a specified character.
Used to find strings when the precise spelling is not known; based on
phonetic assumptions about how certain letters are pronounced.
Used to perform a variety of operations on date, time, and timestamp
fields.
Used to gain insight into the record sequence of a data set or perform
operations based on that sequence.
Used to access global values created by a Set Globals node. For
example, @MEAN is used to refer to the mean average of all values
for a field across the entire data set.
Used to access, flag, and frequently fill user-specified blanks or
system-missing values. For example, @BLANK(FIELD) is used to
raise a true flag for records where blanks are present.
Used to denote the specific fields under examination. For example,
@FIELD is used when deriving multiple fields.

Conventions in Function Descriptions
The following conventions are used throughout this guide when referring to items in a function.
Convention
BOOL
NUM, NUM1, NUM2
REAL, REAL1, REAL2

Description
A Boolean, or flag, such as true or false.
Any number.
Any real number, such as 1.234 or –77.01.

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Convention
INT, INT1, INT2
CHAR
STRING
LIST
ITEM
DATE

Description
Any integer, such as 1 or –77.
A character code, such as `A`.
A string, such as "referrerID".
A list of items, such as ["abc" "def"].
A field, such as Customer or extract_concept.
A date field, such as start_date, where values are in a format
such as DD-MON-YYYY.
A time field, such as power_flux, where values are in a format
such as HHMMSS.

TIME

Functions in this guide are listed with the function in one column, the result type (integer, string,
and so on) in another, and a description (where available) in a third column. For example, the
following is the description of the rem function.
Function

Result

INT1 rem INT2

Number

Description
Returns the remainder of INT1 divided by INT2. For
example, INT1 – (INT1 div INT2) * INT2.

Details on usage conventions, such as how to list items or specify characters in a function, are
described elsewhere. For more information, see the topic CLEM Datatypes on p. 127.

Information Functions
Information functions are used to gain insight into the values of a particular field. They are
typically used to derive flag fields. For example, you can use the @BLANK function to create a flag
field indicating records whose values are blank for the selected field. Similarly, you can check the
storage type for a field using any of the storage type functions, such as is_string.
Function

Result

@BLANK(FIELD)

Boolean

@NULL(ITEM)

Boolean

is_date(ITEM)

Boolean

is_datetime(ITEM)

Boolean

is_integer(ITEM)
is_number(ITEM)
is_real(ITEM)
is_string(ITEM)
is_time(ITEM)
is_timestamp(ITEM)

Boolean
Boolean
Boolean
Boolean
Boolean
Boolean

Description
Returns true for all records whose values are blank
according to the blank-handling rules set in an upstream
Type node or source node (Types tab). Note that this
function cannot be called from a script.
Returns true for all records whose values are undefined.
Undefined values are system null values, displayed in
IBM® SPSS® Modeler as $null$. Note that this function
cannot be called from a script.
Returns true for all records whose type is a date.
Returns true for all records whose type is a date, time,
or timestamp.
Returns true for all records whose type is an integer.
Returns true for all records whose type is a number.
Returns true for all records whose type is a real.
Returns true for all records whose type is a string.
Returns true for all records whose type is a time.
Returns true for all records whose type is a timestamp.

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Conversion Functions
Conversion functions allow you to construct new fields and convert the storage type of existing
files. For example, you can form new strings by joining strings together or by taking strings apart.
To join two strings, use the operator ><. For example, if the field Site has the value "BRAMLEY",
then "xx" >< Site returns "xxBRAMLEY". The result of >< is always a string, even if the arguments are
not strings. Thus, if field V1 is 3 and field V2 is 5, then V1 >< V2 returns "35" (a string, not a number).
Conversion functions (and any other functions that require a specific type of input, such as a
date or time value) depend on the current formats specified in the Stream Options dialog box.
For example, if you want to convert a string field with values Jan 2003, Feb 2003, and so on,
select the matching date format MON YYYY as the default date format for the stream. For more
information, see the topic Setting general options for streams in Chapter 5 on p. 55.
Function
ITEM1 >< ITEM2

Result
String

to_integer(ITEM)
to_real(ITEM)
to_number(ITEM)
to_string(ITEM)
to_time(ITEM)
to_date(ITEM)
to_timestamp(ITEM)
to_datetime(ITEM)

Integer
Real
Number
String
Time
Date
Timestamp
Datetime

datetime_date(ITEM)

Date

Description
Concatenates values for two fields and returns the resulting
string as ITEM1ITEM2.
Converts the storage of the specified field to an integer.
Converts the storage of the specified field to a real.
Converts the storage of the specified field to a number.
Converts the storage of the specified field to a string.
Converts the storage of the specified field to a time.
Converts the storage of the specified field to a date.
Converts the storage of the specified field to a timestamp.
Converts the storage of the specified field to a date, time,
or timestamp value.
Returns the date value for a number, string, or timestamp.
Note this is the only function that allows you to convert a
number (in seconds) back to a date. If ITEM is a string,
creates a date by parsing a string in the current date format.
The date format specified in the stream properties dialog
box must be correct for this function to be successful. If
ITEM is a number, it is interpreted as a number of seconds
since the base date (or epoch). Fractions of a day are
truncated. If ITEM is a timestamp, the date part of the
timestamp is returned. If ITEM is a date, it is returned
unchanged.

Comparison Functions
Comparison functions are used to compare field values to each other or to a specified string. For
example, you can check strings for equality using =. An example of string equality verification
is: Class = "class 1".
For purposes of numeric comparison, greater means closer to positive infinity, and lesser
means closer to negative infinity. That is, all negative numbers are less than any positive number.
Function

Result

count_equal(ITEM1, LIST)

Integer

Description
Returns the number of values from a list of fields that
are equal to ITEM1 or null if ITEM1 is null. For more
information, see the topic Summarizing Multiple Fields in
Chapter 7 on p. 115.

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Function
count_greater_than(ITEM1,
LIST)
count_less_than(ITEM1,
LIST)
count_not_equal(ITEM1,
LIST)
count_nulls(LIST)
count_non_nulls(LIST)
date_before(DATE1,
DATE2)

Result

first_index(ITEM, LIST)

Integer

first_non_null(LIST)

Any

Integer
Integer
Integer
Integer
Integer
Boolean

first_non_null_index(LIST) Integer
ITEM1 = ITEM2

Boolean

ITEM1 /= ITEM2

Boolean

ITEM1 < ITEM2

Boolean

ITEM1 <= ITEM2

Boolean

ITEM1 > ITEM2

Boolean

ITEM1 >= ITEM2

Boolean

last_index(ITEM, LIST)

Integer

last_non_null(LIST)

Any

last_non_null_index(LIST)

Integer

max(ITEM1, ITEM2)

Any

max_index(LIST)

Integer

Description
Returns the number of values from a list of fields that are
greater than ITEM1 or null if ITEM1 is null.
Returns the number of values from a list of fields that are
less than ITEM1 or null if ITEM1 is null.
Returns the number of values from a list of fields that are
not equal to ITEM1 or null if ITEM1 is null.
Returns the number of null values from a list of fields.
Returns the number of non-null values from a list of fields.
Used to check the ordering of date values. Returns a true
value if DATE1 is before DATE2.
Returns the index of the first field containing ITEM from a
LIST of fields or 0 if the value is not found. Supported for
string, integer, and real types only. For more information,
see the topic Working with Multiple-Response Data in
Chapter 7 on p. 117.
Returns the first non-null value in the supplied list of fields.
All storage types supported.
Returns the index of the first field in the specified LIST
containing a non-null value or 0 if all values are null. All
storage types are supported.
Returns true for records where ITEM1 is equal to ITEM2.
Returns true if the two strings are not identical or 0 if they
are identical.
Returns true for records where ITEM1 is less than ITEM2.
Returns true for records where ITEM1 is less than or equal
to ITEM2.
Returns true for records where ITEM1 is greater than
ITEM2.
Returns true for records where ITEM1 is greater than or
equal to ITEM2.
Returns the index of the last field containing ITEM from a
LIST of fields or 0 if the value is not found. Supported for
string, integer, and real types only. For more information,
see the topic Working with Multiple-Response Data in
Chapter 7 on p. 117.
Returns the last non-null value in the supplied list of fields.
All storage types supported.
Returns the index of the last field in the specified LIST
containing a non-null value or 0 if all values are null. All
storage types are supported.
Returns the greater of the two items—ITEM1 or ITEM2.
Returns the index of the field containing the maximum
value from a list of numeric fields or 0 if all values are
null. For example, if the third field listed contains the
maximum, the index value 3 is returned. If multiple fields
contain the maximum value, the one listed first (leftmost)
is returned. For more information, see the topic Working
with Multiple-Response Data in Chapter 7 on p. 117.

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Function

Result

max_n(LIST)

Number

member(ITEM, LIST)

Boolean

min(ITEM1, ITEM2)

Any

min_index(LIST)

Integer

min_n(LIST)

Number

time_before(TIME1,
TIME2)

Boolean

value_at(INT, LIST)

Description
Returns the maximum value from a list of numeric fields or
null if all of the field values are null. For more information,
see the topic Summarizing Multiple Fields in Chapter 7
on p. 115.
Returns true if ITEM is a member of the specified LIST.
Otherwise, a false value is returned. A list of field names
can also be specified. For more information, see the
topic Summarizing Multiple Fields in Chapter 7 on p. 115.
Returns the lesser of the two items—ITEM1 or ITEM2.
Returns the index of the field containing the minimum
value from a list of numeric fields or 0 if all values are
null. For example, if the third field listed contains the
minimum, the index value 3 is returned. If multiple fields
contain the minimum value, the one listed first (leftmost)
is returned. For more information, see the topic Working
with Multiple-Response Data in Chapter 7 on p. 117.
Returns the minimum value from a list of numeric fields or
null if all of the field values are null.
Used to check the ordering of time values. Returns a true
value if TIME1 is before TIME2.
Returns the value of each listed field at offset INT or
NULL if the offset is outside the range of valid values (that
is, less than 1 or greater than the number of listed fields).
All storage types supported.

Logical Functions
CLEM expressions can be used to perform logical operations.
Function

Result

COND1 and COND2

Boolean

COND1 or COND2

Boolean

not(COND)

Boolean

if COND then EXPR1 else
EXPR2 endif

Any

if COND1 then EXPR1 elseif
COND2 then EXPR2 else
EXPR_N endif

Any

Description
This operation is a logical conjunction and returns a true
value if both COND1 and COND2 are true. If COND1 is
false, then COND2 is not evaluated; this makes it possible
to have conjunctions where COND1 first tests that an
operation in COND2 is legal. For example, length(Label)
>=6 and Label(6) = 'x'.
This operation is a logical (inclusive) disjunction and
returns a true value if either COND1 or COND2 is true
or if both are true. If COND1 is true, COND2 is not
evaluated.
This operation is a logical negation and returns a true
value if COND is false. Otherwise, this operation returns
a value of 0.
This operation is a conditional evaluation. If COND
is true, this operation returns the result of EXPR1.
Otherwise, the result of evaluating EXPR2 is returned.
This operation is a multibranch conditional evaluation.
If COND1 is true, this operation returns the result of
EXPR1. Otherwise, if COND2 is true, this operation
returns the result of evaluating EXPR2. Otherwise, the
result of evaluating EXPR_N is returned.

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Numeric Functions
CLEM contains a number of commonly used numeric functions.
Function

Result

–NUM

Number

NUM1 + NUM2
code –NUM2
NUM1 * NUM2
NUM1 / NUM2

Number
Number
Number
Number

INT1 div INT2

Number

INT1 rem INT2

Number

INT1 mod INT2

Number

BASE ** POWER

Number

abs(NUM)

Number

exp(NUM)

Real

fracof(NUM)

Real

intof(NUM)

Integer

log(NUM)

Real

log10(NUM)

Real

negate(NUM)

Number

round(NUM)

Integer

sign(NUM)

Number

sqrt(NUM)

Real

sum_n(LIST)

Number

Description
Used to negate NUM. Returns the corresponding number with
the opposite sign.
Returns the sum of NUM1 and NUM2.
Returns the value of NUM2 subtracted from NUM1.
Returns the value of NUM1 multiplied by NUM2.
Returns the value of NUM1 divided by NUM2.
Used to perform integer division. Returns the value of INT1
divided by INT2.
Returns the remainder of INT1 divided by INT2. For example,
INT1 – (INT1 div INT2) * INT2.
This function has been deprecated. Use the rem function
instead.
Returns BASE raised to the power POWER, where either
may be any number (except that BASE must not be zero if
POWER is zero of any type other than integer 0). If POWER
is an integer, the computation is performed by successively
multiplying powers of BASE. Thus, if BASE is an integer, the
result will be an integer. If POWER is integer 0, the result is
always a 1 of the same type as BASE. Otherwise, if POWER
is not an integer, the result is computed as exp(POWER *
log(BASE)).
Returns the absolute value of NUM, which is always a number
of the same type.
Returns e raised to the power NUM, where e is the base of
natural logarithms.
Returns the fractional part of NUM, defined as
NUM–intof(NUM).
Truncates its argument to an integer. It returns the integer of
the same sign as NUM and with the largest magnitude such
that abs(INT) <= abs(NUM).
Returns the natural (base e) logarithm of NUM, which must
not be a zero of any kind.
Returns the base 10 logarithm of NUM, which must not be
a zero of any kind. This function is defined as log(NUM) /
log(10).
Used to negate NUM. Returns the corresponding number with
the opposite sign.
Used to round NUM to an integer by taking intof(NUM+0.5) if
NUM is positive or intof(NUM–0.5) if NUM is negative.
Used to determine the sign of NUM. This operation returns
–1, 0, or 1 if NUM is an integer. If NUM is a real, it returns
–1.0, 0.0, or 1.0, depending on whether NUM is negative,
zero, or positive.
Returns the square root of NUM. NUM must be positive.
Returns the sum of values from a list of numeric fields or null
if all of the field values are null. For more information, see the
topic Summarizing Multiple Fields in Chapter 7 on p. 115.

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Function

Result

mean_n(LIST)

Number

sdev_n(LIST)

Number

Description
Returns the mean value from a list of numeric fields or null if
all of the field values are null.
Returns the standard deviation from a list of numeric fields
or null if all of the field values are null.

Trigonometric Functions
All of the functions in this section either take an angle as an argument or return one as a result. In
both cases, the units of the angle (radians or degrees) are controlled by the setting of the relevant
stream option.
Function
arccos(NUM)
arccosh(NUM)
arcsin(NUM)
arcsinh(NUM)
arctan(NUM)

Result
Real
Real
Real
Real
Real

arctan2(NUM_Y,
NUM_X)

Real

arctanh(NUM)
cos(NUM)
cosh(NUM)
pi
sin(NUM)
sinh(NUM)
tan(NUM)
tanh(NUM)

Real
Real
Real
Real
Real
Real
Real
Real

Description
Computes the arccosine of the specified angle.
Computes the hyperbolic arccosine of the specified angle.
Computes the arcsine of the specified angle.
Computes the hyperbolic arcsine of the specified angle.
Computes the arctangent of the specified angle.
Computes the arctangent of NUM_Y / NUM_X and uses the
signs of the two numbers to derive quadrant information. The
result is a real in the range - pi < ANGLE <= pi (radians) – 180 <
ANGLE <= 180 (degrees)
Computes the hyperbolic arctangent of the specified angle.
Computes the cosine of the specified angle.
Computes the hyperbolic cosine of the specified angle.
This constant is the best real approximation to pi.
Computes the sine of the specified angle.
Computes the hyperbolic sine of the specified angle.
Computes the tangent of the specified angle.
Computes the hyperbolic tangent of the specified angle.

Probability Functions
Probability functions return probabilities based on various distributions, such as the probability
that a value from Student’s t distribution will be less than a specific value.
Function

Result

cdf_chisq(NUM, DF)

Real

cdf_f(NUM, DF1, DF2)

Real

Description
Returns the probability that a value from the chi-square
distribution with the specified degrees of freedom will
be less than the specified number.
Returns the probability that a value from the F
distribution, with degrees of freedom DF1 and DF2,
will be less than the specified number.

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Function

Result

cdf_normal(NUM, MEAN,
STDDEV)

Real

cdf_t(NUM, DF)

Real

Description
Returns the probability that a value from the normal
distribution with the specified mean and standard
deviation will be less than the specified number.
Returns the probability that a value from Student’s t
distribution with the specified degrees of freedom will
be less than the specified number.

Bitwise Integer Operations
These functions enable integers to be manipulated as bit patterns representing two’s-complement
values, where bit position N has weight 2**N. Bits are numbered from 0 upward. These operations
act as though the sign bit of an integer is extended indefinitely to the left. Thus, everywhere above
its most significant bit, a positive integer has 0 bits and a negative integer has 1 bit.
Note: Bitwise functions cannot be called from scripts.
Function

Result

~~ INT1

Integer

INT1 || INT2

Integer

INT1 ||/& INT2

Integer

INT1 && INT2

Integer

INT1 &&~~ INT2

Integer

INT << N

Integer

INT >> N

Integer

INT1 &&=_0 INT2

Boolean

INT1 &&/=_0 INT2

Boolean

Description
Produces the bitwise complement of the integer INT1.
That is, there is a 1 in the result for each bit position for
which INT1 has 0. It is always true that ~~ INT = –(INT
+ 1). Note that this function cannot be called from a
script.
The result of this operation is the bitwise “inclusive or”
of INT1 and INT2. That is, there is a 1 in the result for
each bit position for which there is a 1 in either INT1
or INT2 or both.
The result of this operation is the bitwise “exclusive
or” of INT1 and INT2. That is, there is a 1 in the result
for each bit position for which there is a 1 in either
INT1 or INT2 but not in both.
Produces the bitwise “and” of the integers INT1 and
INT2. That is, there is a 1 in the result for each bit
position for which there is a 1 in both INT1 and INT2.
Produces the bitwise “and” of INT1and the bitwise
complement of INT2. That is, there is a 1 in the result
for each bit position for which there is a 1 in INT1 and
a 0 in INT2. This is the same as INT1&& (~~INT2) and
is useful for clearing bits of INT1 set in INT2.
Produces the bit pattern of INT1 shifted left by N
positions. A negative value for N produces a right shift.
Produces the bit pattern of INT1 shifted right by N
positions. A negative value for N produces a left shift.
Equivalent to the Boolean expression INT1 && INT2
/== 0 but is more efficient.
Equivalent to the Boolean expression INT1 && INT2
== 0 but is more efficient.

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Function

Result

integer_bitcount(INT)

Integer

integer_leastbit(INT)

Integer

integer_length(INT)

Integer

testbit(INT, N)

Boolean

Description
Counts the number of 1 or 0 bits in the
two’s-complement representation of INT. If
INT is non-negative, N is the number of 1 bits. If
INT is negative, it is the number of 0 bits. Owing to
the sign extension, there are an infinite number of 0
bits in a non-negative integer or 1 bits in a negative
integer. It is always the case that integer_bitcount(INT)
= integer_bitcount(-(INT+1)).
Returns the bit position N of the least-significant bit
set in the integer INT. N is the highest power of 2 by
which INT divides exactly.
Returns the length in bits of INT as a two’s-complement
integer. That is, N is the smallest integer such that INT
< (1 << N) if INT >= 0 INT >= (–1 << N) if INT < 0. If
INT is non-negative, then the representation of INT as
an unsigned integer requires a field of at least N bits.
Alternatively, a minimum of N+1 bits is required to
represent INT as a signed integer, regardless of its sign.
Tests the bit at position N in the integer INT and returns
the state of bit N as a Boolean value, which is true for 1
and false for 0.

Random Functions
The following functions are used to randomly select items or randomly generate numbers.
Function

Result

oneof(LIST)

Any

random(NUM)

Number

random0(NUM)

Number

Description
Returns a randomly chosen element of LIST. List items should
be entered as [ITEM1,ITEM2,...,ITEM_N]. Note that a list of field
names can also be specified. For more information, see the
topic Summarizing Multiple Fields in Chapter 7 on p. 115.
Returns a uniformly distributed random number of the same type
(INT or REAL), starting from 1 to NUM. If you use an integer,
then only integers are returned. If you use a real (decimal)
number, then real numbers are returned (decimal precision
determined by the stream options). The largest random number
returned by the function could equal NUM.
This has the same properties as random(NUM), but starting from
0. The largest random number returned by the function will
never equal X.

String Functions
In CLEM, you can perform the following operations with strings:

Compare strings

Create strings

Access characters

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In CLEM, a string is any sequence of characters between matching double quotation marks
("string quotes"). Characters (CHAR) can be any single alphanumeric character. They are declared
in CLEM expressions using single backquotes in the form of ``, such as `z`, `A`, or `2`.
Characters that are out-of-bounds or negative indices to a string will result in undefined behavior.
Note. Comparisons between strings that do and do not use SQL pushback may generate different
results where trailing spaces exist.
Function

Result

allbutfirst(N, STRING)

String

allbutlast(N, STRING)

String

alphabefore(STRING1, STRING2)

Boolean

endstring(LENGTH, STRING)

String

hasendstring(STRING, SUBSTRING)

Integer

hasmidstring(STRING, SUBSTRING)

Integer

hasstartstring(STRING, SUBSTRING)

Integer

hassubstring(STRING, N, SUBSTRING)

Integer

count_substring(STRING, SUBSTRING)

Integer

hassubstring(STRING, SUBSTRING)

Integer

isalphacode(CHAR)

Boolean

isendstring(SUBSTRING, STRING)

Integer

islowercode(CHAR)

Boolean

Description
Returns a string, which is STRING with the
first N characters removed.
Returns a string, which is STRING with the
last characters removed.
Used to check the alphabetical ordering of
strings. Returns true if STRING1 precedes
STRING2.
Extracts the last N characters from the
specified string. If the string length is less
than or equal to the specified length, then it
is unchanged.
This function is the same as
isendstring(SUBSTRING, STRING).
This function is the same as
ismidstring(SUBSTRING, STRING)
(embedded substring).
This function is the same as
isstartstring(SUBSTRING, STRING).
This function is the same as
issubstring(SUBSTRING, N, STRING),
where N defaults to 1.
Returns the number of times the specified
substring occurs within the string. For
example,
count_substring("foooo.txt", "oo") returns 3.
This function is the same as
issubstring(SUBSTRING, 1, STRING),
where N defaults to 1.
Returns a value of true if CHAR is a character
in the specified string (often a field name)
whose character code is a letter. Otherwise,
this function returns a value of 0. For
example, isalphacode(produce_num(1)).
If the string STRING ends with the substring
SUBSTRING, then this function returns the
integer subscript of SUBSTRING in STRING.
Otherwise, this function returns a value of 0.
Returns a value of true if CHAR is a
lowercase letter character for the specified
string (often a field name). Otherwise,
this function returns a value of 0. For
example, both islowercode(``) and
islowercode(country_name(2)) are valid
expressions.

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Function

Result

ismidstring(SUBSTRING, STRING)

Integer

isnumbercode(CHAR)

Boolean

isstartstring(SUBSTRING, STRING)

Integer

issubstring(SUBSTRING, N, STRING)

Integer

issubstring(SUBSTRING, STRING)

Integer

issubstring_count(SUBSTRING, N, STRING):

Integer

issubstring_lim(SUBSTRING, N, STARTLIM,
ENDLIM, STRING)

Integer

isuppercode(CHAR)

Boolean

last(CHAR)

String

length(STRING)

Integer

Description
If SUBSTRING is a substring of STRING
but does not start on the first character of
STRING or end on the last, then this function
returns the subscript at which the substring
starts. Otherwise, this function returns a
value of 0.
Returns a value of true if CHAR for the
specified string (often a field name) is a
character whose character code is a digit.
Otherwise, this function returns a value of 0.
For example, isnumbercode(product_id(2)).
If the string STRING starts with the substring
SUBSTRING, then this function returns the
subscript 1. Otherwise, this function returns
a value of 0.
Searches the string STRING, starting from
its Nth character, for a substring equal to the
string SUBSTRING. If found, this function
returns the integer subscript at which the
matching substring begins. Otherwise, this
function returns a value of 0. If N is not
given, this function defaults to 1.
Searches the string STRING, starting from
its Nth character, for a substring equal to the
string SUBSTRING. If found, this function
returns the integer subscript at which the
matching substring begins. Otherwise, this
function returns a value of 0. If N is not
given, this function defaults to 1.
Returns the index of the Nth occurrence of
SUBSTRING within the specified STRING.
If there are fewer than N occurrences of
SUBSTRING, 0 is returned.
This function is the same as issubstring,
but the match is constrained to start on
or before the subscript STARTLIM and to
end on or before the subscript ENDLIM.
The STARTLIM or ENDLIM constraints
may be disabled by supplying a value of
false for either argument—for example,
issubstring_lim(SUBSTRING, N, false, false,
STRING) is the same as issubstring.
Returns a value of true if CHAR is an
uppercase letter character. Otherwise,
this function returns a value of 0. For
example, both isuppercode(``) and
isuppercode(country_name(2)) are valid
expressions.
Returns the last character CHAR of STRING
(which must be at least one character long).
Returns the length of the string
STRING—that is, the number of characters
in it.

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Function

Result

locchar(CHAR, N, STRING)

Integer

locchar_back(CHAR, N, STRING)

Integer

lowertoupper(CHAR)
lowertoupper (STRING)

CHAR or
String

matches

Boolean

replace(SUBSTRING, NEWSUBSTRING,
STRING)

String

replicate(COUNT, STRING)

String

Description
Used to identify the location of characters
in symbolic fields. The function searches
the string STRING for the character CHAR,
starting the search at the Nth character
of STRING. This function returns a value
indicating the location (starting at N) where
the character is found. If the character is not
found, this function returns a value of 0. If
the function has an invalid offset (N) (for
example, an offset that is beyond the length
of the string), this function returns $null$.
For example, locchar(`n`, 2, web_page)
searches the field called web_page for the `n`
character beginning at the second character
in the field value.
Note: Be sure to use single backquotes to
encapsulate the specified character.
Similar to locchar, except that the search is
performed backward starting from the Nth
character. For example, locchar_back(`n`,
9, web_page) searches the field web_page
starting from the ninth character and moving
backward toward the start of the string.
If the function has an invalid offset (for
example, an offset that is beyond the length
of the string), this function returns $null$.
Ideally, you should use locchar_back in
conjunction with the function length()
to dynamically use the length of the
current value of the field. For example,
locchar_back(`n`, (length(web_page)),
web_page).
Input can be either a string or character,
which is used in this function to return
a new item of the same type, with any
lowercase characters converted to their
uppercase equivalents. For example,
lowertoupper(`a`), lowertoupper(“My
string”), and lowertoupper(field_name(2))
are all valid expressions.
Returns true if a string matches a specified
pattern. The pattern must be a string literal;
it cannot be a field name containing a pattern.
A question mark (?) can be included in the
pattern to match exactly one character; an
asterisk (*) matches zero or more characters.
To match a literal question mark or asterisk
(rather than using these as wildcards), a
backslash (\) can be used as an escape
character.
Within the specified STRING, replace
all instances of SUBSTRING with
NEWSUBSTRING.
Returns a string that consists of the original
string copied the specified number of times.

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Function

Result

stripchar(CHAR,STRING)

String

skipchar(CHAR, N, STRING)

Integer

skipchar_back(CHAR, N, STRING)

Integer

startstring(LENGTH, STRING)

String

strmember(CHAR, STRING)

Integer

subscrs(N, STRING)

CHAR

substring(N, LEN, STRING)

String

substring_between(N1, N2, STRING)

String

trim(STRING)

String

trim_start(STRING)

String

trimend(STRING)

String

Description
Enables you to remove specified characters
from a string or field. You can use this
function, for example, to remove extra
symbols, such as currency notations, from
data to achieve a simple number or name.
For example, using the syntax stripchar(`$`,
'Cost') returns a new field with the dollar
sign removed from all values.
Note: Be sure to use single backquotes to
encapsulate the specified character.
Searches the string STRING for any
character other than CHAR, starting at the
Nth character. This function returns an
integer substring indicating the point at
which one is found or 0 if every character
from the Nth onward is a CHAR. If the
function has an invalid offset (for example,
an offset that is beyond the length of the
string), this function returns $null$.
locchar is often used in conjunction with the
skipchar functions to determine the value
of N (the point at which to start searching
the string). For example, skipchar(`s`,
(locchar(`s`, 1, "MyString")), "MyString").
Similar to skipchar, except that the search is
performed backward, starting from the Nth
character.
Extracts the first N characters from the
specified string. If the string length is less
than or equal to the specified length, then it
is unchanged.
Equivalent to locchar(CHAR, 1, STRING). It
returns an integer substring indicating the
point at which CHAR first occurs, or 0. If the
function has an invalid offset (for example,
an offset that is beyond the length of the
string), this function returns $null$.
Returns the Nth character CHAR of the input
string STRING. This function can also be
written in a shorthand form as STRING(N).
For example, lowertoupper(“name”(1)) is a
valid expression.
Returns a string SUBSTRING, which consists
of the LEN characters of the string STRING,
starting from the character at subscript N.
Returns the substring of STRING, which
begins at subscript N1 and ends at subscript
N2.
Removes leading and trailing white space
characters from the specified string.
Removes leading white space characters
from the specified string.
Removes trailing white space characters
from the specified string.

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Function

Result

unicode_char(NUM)

CHAR

unicode_value(CHAR)

NUM

uppertolower(CHAR)
uppertolower (STRING)

CHAR or
String

Description
Returns the character with Unicode value
NUM.
Returns the Unicode value of CHAR
Input can be either a string or character and is
used in this function to return a new item of
the same type with any uppercase characters
converted to their lowercase equivalents.
Note: Remember to specify strings with
double quotes and characters with single
backquotes. Simple field names should be
specified without quotes.

SoundEx Functions
SoundEx is a method used to find strings when the sound is known but the precise spelling is not.
Developed in 1918, it searches out words with similar sounds based on phonetic assumptions
about how certain letters are pronounced. It can be used to search names in a database, for
example, where spellings and pronunciations for similar names may vary. The basic SoundEx
algorithm is documented in a number of sources and, despite known limitations (for example,
leading letter combinations such as ph and f will not match even though they sound the same), is
supported in some form by most databases.
Function

Result

soundex(STRING)

Integer

soundex_difference(STRING1, STRING2)

Integer

Description
Returns the four-character SoundEx code for
the specified STRING.
Returns an integer between 0 and 4 that
indicates the number of characters that are
the same in the SoundEx encoding for the
two strings, where 0 indicates no similarity
and 4 indicates strong similarity or identical
strings.

Date and Time Functions
CLEM includes a family of functions for handling fields with datetime storage of string variables
representing dates and times. The formats of date and time used are specific to each stream and
are specified in the stream properties dialog box. The date and time functions parse date and time
strings according to the currently selected format.
When you specify a year in a date that uses only two digits (that is, the century is not specified),
IBM® SPSS® Modeler uses the default century that is specified in the stream properties dialog
box.

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Note: Date and time functions cannot be called from scripts.
Function

Result

@TODAY

String

to_time(ITEM)
to_date(ITEM)
to_timestamp(ITEM)

Time
Date
Timestamp

to_datetime(ITEM)

Datetime

datetime_date(ITEM)

Date

date_before(DATE1, DATE2)

Boolean

date_days_difference(DATE1,
DATE2)

Integer

date_in_days(DATE)

Integer

date_in_months(DATE)

Real

Description
If you select Rollover days/mins in the stream properties
dialog box, this function returns the current date as a
string in the current date format. If you use a two-digit
date format and do not select Rollover days/mins, this
function returns $null$ on the current server. Note that
this function cannot be called from a script.
Converts the storage of the specified field to a time.
Converts the storage of the specified field to a date.
Converts the storage of the specified field to a
timestamp.
Converts the storage of the specified field to a date,
time, or timestamp value.
Returns the date value for a number, string, or
timestamp. Note this is the only function that allows
you to convert a number (in seconds) back to a date.
If ITEM is a string, creates a date by parsing a string
in the current date format. The date format specified
in the stream properties dialog box must be correct for
this function to be successful. If ITEM is a number, it
is interpreted as a number of seconds since the base
date (or epoch). Fractions of a day are truncated. If
ITEM is timestamp, the date part of the timestamp is
returned. If ITEM is a date, it is returned unchanged.
Returns a value of true if DATE1 represents a date
or timestamp before that represented by DATE2.
Otherwise, this function returns a value of 0.
Returns the time in days from the date or timestamp
represented by DATE1 to that represented by DATE2,
as an integer. If DATE2 is before DATE1, this function
returns a negative number.
Returns the time in days from the baseline date to the
date or timestamp represented by DATE, as an integer.
If DATE is before the baseline date, this function
returns a negative number. You must include a valid
date for the calculation to work appropriately. For
example, you should not specify 29 February 2001 as
the date. Because 2001 is a not a leap year, this date
does not exist.
Returns the time in months from the baseline date to
the date or timestamp represented by DATE, as a real
number. This is an approximate figure based on a
month of 30.4375 days. If DATE is before the baseline
date, this function returns a negative number. You
must include a valid date for the calculation to work
appropriately. For example, you should not specify 29
February 2001 as the date. Because 2001 is a not a
leap year, this date does not exist.

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Function

Result

date_in_weeks(DATE)

Real

date_in_years(DATE)

Real

date_months_difference
(DATE1, DATE2)

Real

datetime_date(YEAR, MONTH,
DAY)

Date

datetime_day(DATE)

Integer

datetime_day_name(DAY)

String

datetime_hour(TIME)

Integer

datetime_in_seconds(TIME)
datetime_in_seconds(DATE),
datetime_in_seconds(DATETIME)

Real

datetime_minute(TIME)

Integer

datetime_month(DATE)

Integer

datetime_month_name
(MONTH)
datetime_now

Real

String
Timestamp

datetime_second(TIME)

Integer

datetime_day_short_
name(DAY)

String

datetime_month_short_
name(MONTH)

String

datetime_time(HOUR, MINUTE,
SECOND)

Time

Description
Returns the time in weeks from the baseline date to
the date or timestamp represented by DATE, as a real
number. This is based on a week of 7.0 days. If DATE
is before the baseline date, this function returns a
negative number. You must include a valid date for
the calculation to work appropriately. For example,
you should not specify 29 February 2001 as the date.
Because 2001 is a not a leap year, this date does not
exist.
Returns the time in years from the baseline date to
the date or timestamp represented by DATE, as a real
number. This is an approximate figure based on a
year of 365.25 days. If DATE is before the baseline
date, this function returns a negative number. You
must include a valid date for the calculation to work
appropriately. For example, you should not specify 29
February 2001 as the date. Because 2001 is a not a
leap year, this date does not exist.
Returns the time in months from the date or timestamp
represented by DATE1 to that represented by DATE2,
as a real number. This is an approximate figure based
on a month of 30.4375 days. If DATE2 is before
DATE1, this function returns a negative number.
Creates a date value for the given YEAR, MONTH, and
DAY. The arguments must be integers.
Returns the day of the month from a given DATE or
timestamp. The result is an integer in the range 1 to 31.
Returns the full name of the given DAY. The argument
must be an integer in the range 1 (Sunday) to 7
(Saturday).
Returns the hour from a TIME or timestamp. The
result is an integer in the range 0 to 23.
Returns the seconds portion stored in TIME.
Returns the accumulated number, converted into
seconds, from the difference between the current DATE
or DATETIME and the baseline date (1900-01-01).
Returns the minute from a TIME or timestamp. The
result is an integer in the range 0 to 59.
Returns the month from a DATE or timestamp. The
result is an integer in the range 1 to 12.
Returns the full name of the given MONTH. The
argument must be an integer in the range 1 to 12.
Returns the current time as a timestamp.
Returns the second from a TIME or timestamp. The
result is an integer in the range 0 to 59.
Returns the abbreviated name of the given DAY. The
argument must be an integer in the range 1 (Sunday)
to 7 (Saturday).
Returns the abbreviated name of the given MONTH.
The argument must be an integer in the range 1 to 12.
Returns the time value for the specified HOUR,
MINUTE, and SECOND. The arguments must be
integers.

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Function
datetime_time(ITEM)
datetime_timestamp(YEAR,
MONTH, DAY, HOUR, MINUTE,
SECOND)
datetime_timestamp(DATE,
TIME)

Result
Time

datetime_timestamp (NUMBER)

Timestamp

datetime_weekday(DATE)

Integer

datetime_year(DATE)

Integer

date_weeks_difference (DATE1,
DATE2)

Real

date_years_difference (DATE1,
DATE2)

Real

time_before(TIME1, TIME2)

Boolean

time_hours_difference (TIME1,
TIME2)

Real

time_in_hours(TIME)

Real

time_in_mins(TIME)

Real

time_in_secs(TIME)

Integer

Timestamp
Timestamp

Description
Returns the time value of the given ITEM.
Returns the timestamp value for the given YEAR,
MONTH, DAY, HOUR, MINUTE, and SECOND.
Returns the timestamp value for the given DATE and
TIME.
Returns the timestamp value of the given number of
seconds.
Returns the day of the week from the given DATE or
timestamp.
Returns the year from a DATE or timestamp. The
result is an integer such as 2002.
Returns the time in weeks from the date or timestamp
represented by DATE1 to that represented by DATE2,
as a real number. This is based on a week of 7.0 days.
If DATE2 is before DATE1, this function returns a
negative number.
Returns the time in years from the date or timestamp
represented by DATE1 to that represented by DATE2,
as a real number. This is an approximate figure based
on a year of 365.25 days. If DATE2 is before DATE1,
this function returns a negative number.
Returns a value of true if TIME1 represents a time
or timestamp before that represented by TIME2.
Otherwise, this function returns a value of 0.
Returns the time difference in hours between the times
or timestamps represented by TIME1 and TIME2, as
a real number. If you select Rollover days/mins in the
stream properties dialog box, a higher value of TIME1
is taken to refer to the previous day. If you do not
select the rollover option, a higher value of TIME1
causes the returned value to be negative.
Returns the time in hours represented by TIME, as a
real number. For example, under time format HHMM,
the expression time_in_hours('0130') evaluates to 1.5.
TIME can represent a time or a timestamp.
Returns the time in minutes represented by TIME,
as a real number. TIME can represent a time or a
timestamp.
Returns the time in seconds represented by TIME, as
an integer. TIME can represent a time or a timestamp.

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Function

Result

time_mins_difference(TIME1,
TIME2)

Real

time_secs_difference(TIME1,
TIME2)

Integer

Description
Returns the time difference in minutes between the
times or timestamps represented by TIME1 and
TIME2, as a real number. If you select Rollover
days/mins in the stream properties dialog box, a higher
value of TIME1 is taken to refer to the previous day
(or the previous hour, if only minutes and seconds are
specified in the current format). If you do not select
the rollover option, a higher value of TIME1 will cause
the returned value to be negative.
Returns the time difference in seconds between the
times or timestamps represented by TIME1 and
TIME2, as an integer. If you select Rollover days/mins
in the stream properties dialog box, a higher value
of TIME1 is taken to refer to the previous day (or
the previous hour, if only minutes and seconds are
specified in the current format). If you do not select
the rollover option, a higher value of TIME1 causes
the returned value to be negative.

Converting Date and Time Values
Note that conversion functions (and any other functions that require a specific type of input, such
as a date or time value) depend on the current formats specified in the Stream Options dialog box.
For example, if you have a field named DATE that is stored as a string with values Jan 2003, Feb
2003, and so on, you could convert it to date storage as follows:
to_date(DATE)

For this conversion to work, select the matching date format MON YYYY as the default date
format for the stream. For more information, see the topic Setting general options for streams in
Chapter 5 on p. 55.
For an example that converts string values to dates using a Filler node, see the stream
broadband_create_models.str, installed in the \Demos folder under the streams subfolder.
Dates stored as numbers. Note that DATE in the previous example is the name of a field, while
to_date is a CLEM function. If you have dates stored as numbers, you can convert them using
the datetime_date function, where the number is interpreted as a number of seconds since the
base date (or epoch).
datetime_date(DATE)

By converting a date to a number of seconds (and back), you can perform calculations such as
computing the current date plus or minus a fixed number of days, for example:
datetime_date((date_in_days(DATE)-7)*60*60*24)

Sequence Functions
For some operations, the sequence of events is important. The application allows you to work
with the following record sequences:

Sequences and time series

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Sequence functions

Record indexing

Averaging, summing, and comparing values

Monitoring change—differentiation

@SINCE

Offset values

Additional sequence facilities

For many applications, each record passing through a stream can be considered as an individual
case, independent of all others. In such situations, the order of records is usually unimportant.
For some classes of problems, however, the record sequence is very important. These are
typically time series situations, in which the sequence of records represents an ordered sequence
of events or occurrences. Each record represents a snapshot at a particular instant in time; much
of the richest information, however, might be contained not in instantaneous values but in the
way in which such values are changing and behaving over time.
Of course, the relevant parameter may be something other than time. For example, the records
could represent analyses performed at distances along a line, but the same principles would apply.
Sequence and special functions are immediately recognizable by the following characteristics:

They are all prefixed by @.

Their names are given in upper case.

Sequence functions can refer to the record currently being processed by a node, the records that
have already passed through a node, and even, in one case, records that have yet to pass through
a node. Sequence functions can be mixed freely with other components of CLEM expressions,
although some have restrictions on what can be used as their arguments.
Examples

You may find it useful to know how long it has been since a certain event occurred or a condition
was true. Use the function @SINCE to do this—for example:
@SINCE(Income > Outgoings)

This function returns the offset of the last record where this condition was true—that is, the
number of records before this one in which the condition was true. If the condition has never been
true, @SINCE returns @INDEX + 1.
Sometimes you may want to refer to a value of the current record in the expression used by
@SINCE. You can do this using the function @THIS, which specifies that a field name always
applies to the current record. To find the offset of the last record that had a Concentration field
value more than twice that of the current record, you could use:
@SINCE(Concentration > 2 * @THIS(Concentration))

In some cases the condition given to @SINCE is true of the current record by definition—for
example:
@SINCE(ID == @THIS(ID))

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For this reason, @SINCE does not evaluate its condition for the current record. Use a similar
function, @SINCE0, if you want to evaluate the condition for the current record as well as previous
ones; if the condition is true in the current record, @SINCE0 returns 0.
Note: @ functions cannot be called from scripts.
Function

Result

MEAN(FIELD)

Real

@MEAN(FIELD, EXPR)

Real

@MEAN(FIELD, EXPR, INT)

Real

@DIFF1(FIELD)

Real

@DIFF1(FIELD1, FIELD2)

Real

@DIFF2(FIELD)

Real

@DIFF2(FIELD1, FIELD2)

Real

@INDEX

Integer

@LAST_NON_BLANK(FIELD)

Any

@MAX(FIELD)

Number

Description
Returns the mean average of values for the specified
FIELD or FIELDS.
Returns the mean average of values for FIELD over
the last EXPR records received by the current node,
including the current record. FIELD must be the name
of a numeric field. EXPR may be any expression
evaluating to an integer greater than 0. If EXPR is
omitted or if it exceeds the number of records received
so far, the average over all of the records received
so far is returned. Note that this function cannot be
called from a script.
Returns the mean average of values for FIELD over
the last EXPR records received by the current node,
including the current record. FIELD must be the name
of a numeric field. EXPR may be any expression
evaluating to an integer greater than 0. If EXPR is
omitted or if it exceeds the number of records received
so far, the average over all of the records received so
far is returned. INT specifies the maximum number
of values to look back. This is far more efficient than
using just two arguments.
Returns the first differential of FIELD1. The
single-argument form thus simply returns the
difference between the current value and the previous
value of the field. Returns 0 if the relevant previous
records do not exist.
The two-argument form gives the first differential of
FIELD1 with respect to FIELD2. Returns 0 if the
relevant previous records do not exist.
Returns the second differential of FIELD1. The
single-argument form thus simply returns the
difference between the current value and the previous
value of the field. Returns 0 if the relevant previous
records do not exist
The two-argument form gives the first differential of
FIELD1 with respect to FIELD2. Returns 0 if the
relevant previous records do not exist.
Returns the index of the current record. Indices are
allocated to records as they arrive at the current node.
The first record is given index 1, and the index is
incremented by 1 for each subsequent record.
Returns the last value for FIELD that was not blank,
as defined in an upstream source or Type node.
If there are no nonblank values for FIELD in the
records read so far, $null$ is returned. Note that blank
values, also called user-missing values, can be defined
separately for each field.
Returns the maximum value for the specified FIELD.

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Function

Result

@MAX(FIELD, EXPR)

Number

@MAX(FIELD, EXPR, INT)

Number

@MIN(FIELD)

Number

@MIN(FIELD, EXPR)

Number

@MIN(FIELD, EXPR, INT)

Number

@OFFSET(FIELD, EXPR)

Any

Description
Returns the maximum value for FIELD over the last
EXPR records received so far, including the current
record. FIELD must be the name of a numeric field.
EXPR may be any expression evaluating to an integer
greater than 0.
Returns the maximum value for FIELD over the last
EXPR records received so far, including the current
record. FIELD must be the name of a numeric field.
EXPR may be any expression evaluating to an integer
greater than 0. If EXPR is omitted, or if it exceeds the
number of records received so far, the maximum value
over all of the records received so far is returned.
INT specifies the maximum number of values to look
back. This is far more efficient than using just two
arguments.
Returns the minimum value for the specified FIELD.
Returns the minimum value for FIELD over the last
EXPR records received so far, including the current
record. FIELD must be the name of a numeric field.
EXPR may be any expression evaluating to an integer
greater than 0.
Returns the minimum value for FIELD over the last
EXPR records received so far, including the current
record. FIELD must be the name of a numeric field.
EXPR may be any expression evaluating to an integer
greater than 0. If EXPR is omitted, or if it exceeds the
number of records received so far, the minimum value
over all of the records received so far is returned.
INT specifies the maximum number of values to look
back. This is far more efficient than using just two
arguments.
Returns the value of FIELD in the record offset from
the current record by the value of EXPR. A positive
offset refers to a record that has already passed, while
a negative one specifies a “lookahead” to a record that
has yet to arrive. For example, @OFFSET(Status, 1)
returns the value of the Status field in the previous
record, while @OFFSET(Status, –4) “looks ahead”
four records in the sequence (that is, to records that
have not yet passed through this node) to obtain the
value. Note that a negative (look ahead) offset must
be specified as a constant. For positive offsets only,
EXPR may also be an arbitrary CLEM expression,
which is evaluated for the current record to give the
offset. In this case, the three-argument version of
this function should improve performance (see next
function). If the expression returns anything other
than a non-negative integer, this causes an error—that
is, it is not legal to have calculated lookahead offsets.
Note: A self-referential @OFFSET function cannot use
literal lookahead. For example, in a Filler node, you
cannot replace the value of field1 using an expression
such as @OFFSET(field1,-2).

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Function

Result

@OFFSET(FIELD, EXPR, INT)

Any

@SDEV(FIELD)

Real

@SDEV(FIELD, EXPR)

Real

@SDEV(FIELD, EXPR, INT)

Real

@SINCE(EXPR)

Any

@SINCE(EXPR, INT)

Any

@SINCE0(EXPR)

Any

@SINCE0(EXPR, INT)

Any

@SUM(FIELD)

Number

Description
Performs the same operation as the @OFFSET function
with the addition of a third argument, INT, which
specifies the maximum number of values to look
back. In cases where the offset is computed from
an expression, this third argument should improve
performance.
For example, in an expression such as@OFFSET(Foo,
Month, 12), the system knows to keep only the last
twelve values of Foo; otherwise, it has to store
every value just in case. In cases where the offset
value is a constant—including negative “lookahead”
offsets, which must be constant—the third argument
is pointless and the two-argument version of this
function should be used. See also the note about
self-referential functions in the two-argument version
described earlier.
Returns the standard deviation of values for the
specified FIELD or FIELDS.
Returns the standard deviation of values for FIELD
over the last EXPR records received by the current
node, including the current record. FIELD must
be the name of a numeric field. EXPR may be any
expression evaluating to an integer greater than 0.
If EXPR is omitted, or if it exceeds the number of
records received so far, the standard deviation over all
of the records received so far is returned.
Returns the standard deviation of values for FIELD
over the last EXPR records received by the current
node, including the current record. FIELD must be the
name of a numeric field. EXPR may be any expression
evaluating to an integer greater than 0. If EXPR
is omitted, or if it exceeds the number of records
received so far, the standard deviation over all of the
records received so far is returned. INT specifies the
maximum number of values to look back. This is far
more efficient than using just two arguments.
Returns the number of records that have passed since
EXPR, an arbitrary CLEM expression, was true.
Adding the second argument, INT, specifies the
maximum number of records to look back. If EXPR
has never been true, INT is @INDEX+1.
Considers the current record, while @SINCE does not;
@SINCE0 returns 0 if EXPR is true for the current
record.
Adding the second argument, INT specifies the
maximum number of records to look back.
Returns the sum of values for the specified FIELD
or FIELDS.

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Function

Result

@SUM(FIELD, EXPR)

Number

@SUM(FIELD, EXPR, INT)

Number

@THIS(FIELD)

Any

Description
Returns the sum of values for FIELD over the last
EXPR records received by the current node, including
the current record. FIELD must be the name of
a numeric field. EXPR may be any expression
evaluating to an integer greater than 0. If EXPR
is omitted, or if it exceeds the number of records
received so far, the sum over all of the records
received so far is returned.
Returns the sum of values for FIELD over the
last EXPR records received by the current node,
including the current record. FIELD must be
the name of a numeric field. EXPR may be any
expression evaluating to an integer greater than 0.
If EXPR is omitted, or if it exceeds the number
of records received so far, the sum over all of the
records received so far is returned. INT specifies the
maximum number of values to look back. This is far
more efficient than using just two arguments.
Returns the value of the field named FIELD in the
current record. Used only in @SINCE expressions.

Global Functions
The functions @MEAN,@SUM, @MIN, @MAX, and @SDEV work on, at most, all of the records
read up to and including the current one. In some cases, however, it is useful to be able to work
out how values in the current record compare with values seen in the entire data set. Using a
Set Globals node to generate values across the entire data set, you can access these values in a
CLEM expression using the global functions.
For example,
@GLOBAL_MAX(Age)

returns the highest value of Age in the data set, while the expression
(Value - @GLOBAL_MEAN(Value)) / @GLOBAL_SDEV(Value)

expresses the difference between this record’s Value and the global mean as a number of standard
deviations. You can use global values only after they have been calculated by a Set Globals
node. All current global values can be canceled by clicking the Clear Global Values button on the
Globals tab in the stream properties dialog box.
Note: @ functions cannot be called from scripts.
Function

Result

@GLOBAL_MAX(FIELD)

Number

Description
Returns the maximum value for FIELD over the whole
data set, as previously generated by a Set Globals node.
FIELD must be the name of a numeric field. If the
corresponding global value has not been set, an error
occurs. Note that this function cannot be called from a
script.

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Function

Result

@GLOBAL_MIN(FIELD)

Number

@GLOBAL_SDEV(FIELD)

Number

@GLOBAL_MEAN(FIELD)

Number

@GLOBAL_SUM(FIELD)

Number

Description
Returns the minimum value for FIELD over the whole
data set, as previously generated by a Set Globals node.
FIELD must be the name of a numeric field. If the
corresponding global value has not been set, an error
occurs.
Returns the standard deviation of values for FIELD over
the whole data set, as previously generated by a Set
Globals node. FIELD must be the name of a numeric
field. If the corresponding global value has not been set,
an error occurs.
Returns the mean average of values for FIELD over the
whole data set, as previously generated by a Set Globals
node. FIELD must be the name of a numeric field. If
the corresponding global value has not been set, an error
occurs.
Returns the sum of values for FIELD over the whole
data set, as previously generated by a Set Globals node.
FIELD must be the name of a numeric field. If the
corresponding global value has not been set, an error
occurs.

Functions Handling Blanks and Null Values
Using CLEM, you can specify that certain values in a field are to be regarded as “blanks,” or
missing values. The following functions work with blanks.
Note: @ functions cannot be called from scripts.
Function

Result

@BLANK(FIELD)

Boolean

@LAST_NON_BLANK(FIELD)

Any

@NULL(FIELD)

Boolean

undef

Any

Description
Returns true for all records whose values are blank
according to the blank-handling rules set in an
upstream Type node or source node (Types tab).
Note that this function cannot be called from a script.
Returns the last value for FIELD that was not blank,
as defined in an upstream source or Type node.
If there are no nonblank values for FIELD in the
records read so far, $null$ is returned. Note that
blank values, also called user-missing values, can be
defined separately for each field.
Returns true if the value of FIELD is the
system-missing $null$. Returns false for all other
values, including user-defined blanks. If you
want to check for both, use @BLANK(FIELD)and
@NULL(FIELD).
Used generally in CLEM to enter a $null$ value—for
example, to fill blank values with nulls in the Filler
node.

Blank fields may be “filled in” with the Filler node. In both Filler and Derive nodes (multiple
mode only), the special CLEM function @FIELD refers to the current field(s) being examined.

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Special Fields
Special functions are used to denote the specific fields under examination, or to generate a list
of fields as input. For example, when deriving multiple fields at once, you should use @FIELD
to denote “perform this derive action on the selected fields.” Using the expression log(@FIELD)
derives a new log field for each selected field.
Note: @ functions cannot be called from scripts.
Function

Result

@FIELD

Any

@TARGET

Any

@PREDICTED

Any

@PARTITION_FIELD

Any

@TRAINING_PARTITION

Any

@TESTING_PARTITION

Any

@VALIDATION_PARTITION

Any

@FIELDS_BETWEEN(start, end)

Any

@FIELDS_MATCHING(pattern)

Any

@MULTI_RESPONSE_SET

Any

Description
Performs an action on all fields specified in
the expression context. Note that this function
cannot be called from a script.
When a CLEM expression is used in a
user-defined analysis function, @TARGET
represents the target field or “correct value” for
the target/predicted pair being analyzed. This
function is commonly used in an Analysis node.
When a CLEM expression is used in a
user-defined analysis function,@PREDICTED
represents the predicted value for the
target/predicted pair being analyzed. This
function is commonly used in an Analysis node.
Substitutes the name of the current partition
field.
Returns the value of the current training
partition. For example, to select training records
using a Select node, use the CLEM expression:
@PARTITION_FIELD = @TRAINING_PARTITION
This ensures that the Select node will always
work regardless of which values are used to
represent each partition in the data.
Returns the value of the current testing partition.
Returns the value of the current validation
partition.
Returns the list of field names between the
specified start and end fields (inclusive) based
on the natural (that is, insert) order of the fields
in the data. For more information, see the
topic Summarizing Multiple Fields in Chapter 7
on p. 115.
Returns a list a field names matching a specified
pattern. A question mark (?) can be included in
the pattern to match exactly one character; an
asterisk (*) matches zero or more characters. To
match a literal question mark or asterisk (rather
than using these as wildcards), a backslash
(\) can be used as an escape character. For
more information, see the topic Summarizing
Multiple Fields in Chapter 7 on p. 115.
Returns the list of fields in the named multiple
response set. For more information, see the
topic Working with Multiple-Response Data in
Chapter 7 on p. 117.

Chapter

Using IBM SPSS Modeler with a
Repository

About the IBM SPSS Collaboration and Deployment Services Repository
IBM® SPSS® Modeler can be used in conjunction with an IBM SPSS Collaboration and
Deployment Services repository, enabling you to manage the life cycle of data mining models
and related predictive objects, and enabling these objects to be used by enterprise applications,
tools, and solutions. SPSS Modeler objects that can be shared in this way include streams, nodes,
stream outputs, scenarios, projects, and models. Objects are stored in the central repository,
from where they can be shared with other applications and tracked using extended versioning,
metadata, and search capabilities.
Note: A separate license is required to access an IBM® SPSS®
Collaboration and Deployment Services repository. For more information, see
http://www.ibm.com/software/analytics/spss/products/deployment/cds/
Before you can use SPSS Modeler with the repository, you need to install an adapter at the
repository host. Without this adapter, you may see the following message when attempting to
access repository objects from certain SPSS Modeler nodes or models:
The repository may need updating to support new node, model and output types.

For instructions on installing the adapter, see the SPSS Modeler Deployment Installation guide,
available on the SPSS Modeler Deployment DVD. Details of how to access SPSS Modeler
repository objects from IBM® SPSS® Collaboration and Deployment Services Deployment
Manager are given in the SPSS Modeler Deployment Guide.
The following sections provide information on accessing the repository from within SPSS
Modeler.

158

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Using IBM SPSS Modeler with a Repository
Figure 9-1
Objects in the IBM SPSS Collaboration and Deployment Services Repository

Extensive Versioning and Search Support

The repository provides comprehensive object versioning and search capabilities. For example,
suppose that you create a stream and store it in the repository where it can be shared with
researchers from other divisions. If you later update the stream in SPSS Modeler, you can add
the updated version to the repository without overwriting the previous version. All versions
remain accessible and can be searched by name, label, fields used, or other attributes. You could,
for example, search for all model versions that use net revenue as an input, or all models created
by a particular author. (To do this with a traditional file system, you would have to save each
version under a different filename, and the relationships between versions would be unknown
to the software.)
Single Sign-On

The single sign-on feature enables users to connect to the repository without having to enter
username and password details each time. The user’s existing local network login details provide
the necessary authentication to IBM SPSS Collaboration and Deployment Services. This feature
depends on the following:

IBM SPSS Collaboration and Deployment Services must be configured to use a single sign-on
provider.

The user must be logged in to a host that is compatible with the provider.

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For more information, see the topic Connecting to the Repository on p. 161.

Storing and Deploying Repository Objects
Streams created in IBM® SPSS® Modeler can be stored in the repository just as they are, as files
with the extension .str. In this way, a single stream can be accessed by multiple users throughout
the enterprise. For more information, see the topic Storing Objects in the Repository on p. 164.
It is also possible to deploy a stream in the repository. A deployed stream is stored as a file with
additional metadata. A deployed stream can take full advantage of the enterprise-level features
of IBM SPSS Collaboration and Deployment Services, such as automated scoring and model
refresh. For example, a model can be automatically updated at regularly-scheduled intervals
as new data becomes available. Alternatively, a set of streams can be deployed for Champion
Challenger analysis, in which streams are compared to determine which one contains the most
effective predictive model.
You can deploy a stream in one of two ways: as a stream (with the extension .str), or as a scenario
(with the extension .scn). Deployment as a stream enables the stream to be used by the thin-client
application IBM® SPSS® Modeler Advantage. For more information, see the topic Opening a
Stream in IBM SPSS Modeler Advantage in Chapter 10 on p. 195. Deployment as a scenario
enables the stream to be used by Predictive Applications version 5, the predecessor of IBM
SPSS Modeler Advantage.
For more information, see Stream Deployment Options on p. 185.
Requirements for Streams Deployed as Scenarios

To ensure consistent access to enterprise data, streams deployed as scenarios must be accessed
through the Enterprise View component of IBM® SPSS® Collaboration and Deployment
Services. This means that in SPSS Modeler, there must be at least one Enterprise View source
node within each designated scoring or modeling branch in the stream.

To use the Enterprise View node, IBM SPSS Collaboration and Deployment Services
must be installed, configured and accessible from your site, with an Enterprise View,
Application Views, and Data Provider Definitions (DPDs) already defined. For
more information, contact your local administrator, or see the corporate website at
http://www.ibm.com/software/analytics/spss/products/deployment/cds/.

A DPD is defined against a particular ODBC data source. To use a DPD from SPSS Modeler,
you must have an ODBC data source defined on the SPSS Modeler server host that has the
same name and that connects to the same data store as the one referenced in the DPD.

In addition, the IBM® SPSS® Collaboration and Deployment Services Enterprise View
Driver must be installed on each computer used to modify or run the stream. For Windows,
simply install the driver on the computer where SPSS Modeler or SPSS Modeler Server is
installed, and no further configuration of the driver is needed. On UNIX, a reference to the
pev.sh script must be added to the startup script. Contact your local administrator for details
on installing the IBM SPSS Collaboration and Deployment Services Enterprise View Driver.

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Using IBM SPSS Modeler with a Repository

Other Deployment Options

While IBM SPSS Collaboration and Deployment Services offers the most extensive features for
managing enterprise content, a number of other mechanisms for deploying or exporting streams
are also available, including:

Export the stream and model for later use with IBM® SPSS® Modeler Solution Publisher
Runtime.

Export one or more models in PMML, an XML-based format for encoding model information.
For more information, see the topic Importing and Exporting Models as PMML in Chapter 10
on p. 196.

Connecting to the Repository
E To connect to the repository, on the IBM® SPSS® Modeler main menu, click:
Tools > Repository > Options...
E Specify login options as required.

Settings are specific to each site or installation. For specific port and other login details, contact
your local system administrator.
Note: A separate license is required to access an IBM® SPSS®
Collaboration and Deployment Services repository. For more information, see
http://www.ibm.com/software/analytics/spss/products/deployment/cds/
Figure 9-2
IBM SPSS Collaboration and Deployment Services Repository Login

Repository. The repository installation you want to access. Generally, this matches the name of the
host server where the repository is installed. You can connect to only one repository at a time.
Port. The port used to host the connection, typically 8080 by default.
Set Credentials. Leave this box unchecked to enable the single sign-on feature, which attempts
to log you in using your local computer username and password details. If single sign-on is
not possible, or if you check this box to disable single sign-on (for example, to log in to an
administrator account), a further screen is displayed for you to enter your credentials.

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Ensure secure connection. Specifies whether a Secure Sockets Layer (SSL) connection should
be used. SSL is a commonly used protocol for securing data sent over a network. To use this
feature, SSL must be enabled on the server hosting the repository. If necessary, contact your
local administrator for details.

Entering Credentials for the Repository
Figure 9-3
Entering IBM SPSS Collaboration and Deployment Services Repository credentials

User ID and password. Specify a valid user name and password for logging on. If necessary,
contact your local administrator for more information.
Provider. Choose a security provider for authentication. The repository can be configured to use
different security providers; if necessary, contact your local administrator for more information.
Remember repository and user ID. Saves the current settings as the default so that you do not have
to reenter them each time you want to connect.

Browsing the Repository Contents
The repository allows you to browse stored content in a manner similar to Windows Explorer; you
can also browse versions of each stored object.
E To open the IBM® SPSS® Collaboration and Deployment Services Repository window, on the

SPSS Modeler menus click:
Tools > Repository > Explore...
E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository on p. 161. For specific port, password, and other connection
details, contact your local system administrator.

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Figure 9-4
Browsing the IBM SPSS Collaboration and Deployment Services Repository contents

The explorer window initially displays a tree view of the folder hierarchy. Click a folder name to
display its contents.
Objects that match the current selection or search criteria are listed in the right pane, with detailed
information on the selected version displayed in the lower right pane. The attributes displayed
apply to the most recent version.

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Storing Objects in the Repository
Figure 9-5
Storing a model

You can store streams, nodes, models, model palettes, projects, and output objects in the
repository, from where they can be accessed by other users and applications.
Note: A separate license is required to access an IBM® SPSS®
Collaboration and Deployment Services repository. For more information, see
http://www.ibm.com/software/analytics/spss/products/deployment/cds/
You can also publish stream output to the repository in a format that enables other users to view it
over the Internet using the IBM® SPSS® Collaboration and Deployment Services Deployment
Portal.

Setting Object Properties
When you store an object, the Repository: Store dialog box is displayed, enabling you to set the
values of a number of properties for the object. You can:

Choose the name and repository folder under which the object is to be stored

Add information about the object such as the version label and other searchable properties

Assign one or more classification topics to the object

Set security options for the object

The following sections describe the properties you can set.

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Choosing the Location for Storing Objects
Figure 9-6
Choosing the location for storing an object

Save in. Shows the current folder—the location where the object will be stored. Double-click a
folder name in the list to set that folder as the current folder. Use the Up Folder button to navigate
to the parent folder. Use the New Folder button to create a folder at the current level.
File name. The name under which the object will be stored.
Store. Stores the object at the current location.

Adding Information About Stored Objects
All of the fields on this tab are optional.

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Figure 9-7
Adding information about the object

Author. The username of the user creating the object in the repository. By default, this shows the
username used for the repository connection, but you can change this name here.
Version Label. Select a label from the list to indicate the object version, or click Add to create a
new label. Avoid using the “[” character in the label. Ensure that no boxes are checked if you do
not want to assign a label to this object version. For more information, see the topic Viewing and
Editing Object Properties on p. 180.
Description. A description of the object. Users can search for objects by description (see note).
Keywords. One or more keywords that relate to the object and which can be used for search
purposes (see note).
Expiration. A date after which the object is no longer visible to general users, although it can still
be seen by its owner and by the repository administrator. To set an expiration date, select the Date
option and enter the date, or choose one using the calendar button.
Store. Stores the object at the current location.

Note: Information in the Description and Keywords fields is treated as distinct from anything
entered in SPSS Modeler on the Annotations tab of the object. A repository search by description
or keyword does not return information from the Annotations tab. For more information, see the
topic Searching for Objects in the Repository on p. 175.

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Assigning Topics to a Stored Object
Topics are a hierarchical classification system for the content stored in the repository. You can
choose from the available topics when storing objects, and users can also search for objects by
topic. The list of available topics is set by repository users with the appropriate privileges (for
more information, see the Deployment Manager User’s Guide).
Figure 9-8
Assigning topics to an object

To assign a topic to the object:
E Click the Add button.
E Click a topic name from the list of available topics.
E Click OK.

To remove a topic assignment:
E Select the topic in the list of assigned topics.
E Click Delete.

Setting Security Options for Stored Objects
You can set or change a number of security options for a stored object. For one or more principals
(that is, users or groups of users), you can:

Assign access rights to the object

Modify access rights to the object

Remove access rights to the object

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Figure 9-9
Setting security options for an object

Principal. The repository username of the user or group who has access rights on this object.
Permissions. The access rights that this user or group has for the object.
Add. Enables you to add one or more users or groups to the list of those with access rights on this
object. For more information, see the topic Adding a User to the Permissions List on p. 169.
Modify. Enables you to modify the access rights of the selected user or group for this object. Read
access is granted by default. This option enables you to grant additional access rights, namely
Owner, Write, Delete, and Modify Permissions.
Delete. Deletes the selected user or group from the permissions list for this object.

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Adding a User to the Permissions List
Figure 9-10
Adding a user to the permissions list for an object

Select provider. Choose a security provider for authentication. The repository can be configured
to use different security providers; if necessary, contact your local administrator for more
information.
Find. Enter the repository username of the user or group you want to add, and click Search to
display that name in the user list. To add more than one username at a time, leave this field blank
and just click Search to display a list of all the repository usernames.
User list. Select one or more usernames from the list and click OK to add them to the permissions
list.

Modifying Access Rights for an Object
Figure 9-11
Modifying access rights for an object

Owner. Select this option to give this user or group owner access rights to the object. The owner
has full control over the object, including Delete and Modify access rights.

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Read. By default, a user or group that is not the object owner has only Read access rights to the
object. Select the appropriate check boxes to add Write, Delete, and Modify Permissions access
rights for this user or group.

Storing Streams
You can store a stream as a .str file in the repository, from where it can be accessed by other users.
Note: For information on deploying a stream, to take advantage of additional repository features,
see Deploying Streams on p. 184.
To store the current stream:
E On the main menu, click:
File > Store > Store as Stream...
E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository on p. 161. For specific port, password, and other connection
details, contact your local system administrator.
E In the Repository: Store dialog box, choose the folder where you want to store the object, specify
any other information you want to record, and click the Store button. For more information, see

the topic Setting Object Properties on p. 164.

Storing Projects
You can store a complete IBM® SPSS® Modeler project as a .cpj file in the repository so that
it can be accessed by other users.
Because a project file is a container for other SPSS Modeler objects, you need to tell SPSS Modeler
to store the project’s objects in the repository. You do this using a setting in the Project Properties
dialog box. For more information, see the topic Setting Project Properties in Chapter 11 on p. 205.
Once you configure a project to store objects in the repository, whenever you add a new object to
the project, SPSS Modeler automatically prompts you to store the object.
When you have finished your SPSS Modeler session, you must store a new version of the project
file so that it remembers your additions. The project file automatically contains (and retrieves) the
latest versions of its objects. If you did not add any objects to a project during an SPSS Modeler
session, then you do not have to re-store the project file. You must, however, store new versions
for the project objects (streams, output, and so forth) that you changed.
To store a project
E Select the project on the CRISP-DM or Classes tab in the managers pane in SPSS Modeler,

and on the main menu click:
File > Project > Store Project...

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E Specify connection settings to the repository if necessary. For more information, see the

the topic Setting Object Properties on p. 164.

Storing Nodes
You can store an individual node definition from the current stream as a .nod file in the repository,
from where it can be accessed by other users.
To store a node:
E Right-click the node in the stream canvas and click Store Node.
E Specify connection settings to the repository if necessary. For more information, see the

the topic Setting Object Properties on p. 164.

Storing Output Objects
You can store an output object from the current stream as a .cou file in the repository, from where
it can be accessed by other users.
To store an output object:
E Click the object on the Outputs tab of the managers pane in SPSS Modeler, and on the main

menu click:
File > Outputs > Store Output...
E Alternatively, right-click an object in the Outputs tab and click Store.
E Specify connection settings to the repository if necessary. For more information, see the

the topic Setting Object Properties on p. 164.

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Storing Models and Model Palettes
You can store an individual model as a .gm file in the repository, from where it can be accessed
by other users. You can also store the complete contents of the Models palette as a .gen file in
the repository.
Storing a model
E Click the object on the Models palette in SPSS Modeler, and on the main menu click:
File > Models > Store Model...
E Alternatively, right-click an object in the Models palette and click Store Model.
E Continue from “Completing the storage procedure”.

Storing a Models palette
E Right-click the background of the Models palette.
E On the pop-up menu, click Store Palette.
E Continue from “Completing the storage procedure”.

Completing the storage procedure
E Specify connection settings to the repository if necessary. For more information, see the

the topic Setting Object Properties on p. 164.

Retrieving Objects from the Repository
You can retrieve streams, models, model palettes, nodes, projects, and output objects that have
been stored in the repository.
Note: Besides using the menu options as described here, you can also retrieve streams, output
objects, models and model palettes by right-clicking in the appropriate tab of the managers pane at
the top right of the SPSS Modeler window.
E To retrieve a stream, on the IBM® SPSS® Modeler main menu click:
File > Retrieve Stream...
E To retrieve a model, model palette, project, or output object, on the SPSS Modeler main menu

click:
File > Models > Retrieve Model...

or
File > Models > Retrieve Models Palette...

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or
File > Projects > Retrieve Project...

or
File > Outputs > Retrieve Output...
E Alternatively, right-click in the managers or project pane and click Retrieve on the pop-up menu.
E To retrieve a node, on the SPSS Modeler main menu click:
Insert > Node (or SuperNode) from Repository...
E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository on p. 161. For specific port, password, and other connection
details, contact your local system administrator.
E In the Repository: Retrieve dialog box, browse to the object, select it and click the Retrieve button.

Choosing an Object to Retrieve
Figure 9-12
Retrieving an object from the IBM SPSS Collaboration and Deployment Services Repository

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Look in. Shows the folder hierarchy for the current folder. To navigate to a different folder, select
one from this list to navigate there directly, or navigate using the object list below this field.
Up Folder button. Navigates to one level above the current folder in the hierarchy.
New Folder button. Creates a new folder at the current level in the hierarchy.
File name. The repository file name of the selected object. To retrieve that object, click Retrieve.
Files of type. The type of object that you have chosen to retrieve. Only objects of this type, together
with folders, are shown in the object list. To display objects of a different type for retrieval,
select the object type from the list.
Open as locked. By default, when an object is retrieved, it is locked in the repository so that others
cannot update it. If you do not want the object to be locked on retrieval, uncheck this box.
Description, Keywords. If additional details about the object were defined when the object was
stored, those details are displayed here. For more information, see the topic Adding Information
About Stored Objects on p. 165.
Version. To retrieve a version of the object other than the latest, click this button. Detailed
information for all versions is displayed, allowing you to choose the version you want.

Selecting an Object Version
Figure 9-13
Selecting a version of an object

To select a specific version of a repository object.
E (Optional) Sort the list by version, label, size, creation date or creating user, by double-clicking on

the header of the appropriate column.

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E Select the object version you want to work with.
E Click Continue.

Searching for Objects in the Repository
You can search for objects by name, folder, type, label, date, or other criteria.
Searching by Name

To search for objects by name:
E On the IBM® SPSS® Modeler main menu click:
Tools > Repository > Explore...
E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository on p. 161. For specific port, password, and other connection
details, contact your local system administrator.
E Click the Search tab.
E In the Search for objects named field, specify the name of the object you want to find.
Figure 9-14
Searching for objects by name

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When searching for objects by name, an asterisk (*) can be used as a wildcard character to match
any string of characters, and a question mark (?) matches any single character. For example,
*cluster* matches all objects that include the string cluster anywhere in the name. The search
string m0?_* matches M01_cluster.str and M02_cluster.str but not M01a_cluster.str. Searches are
not case sensitive (cluster matches Cluster matches CLUSTER).
Note: If the number of objects is large, searches may take a few moments.
Searching by Other Criteria

You can perform a search based on title, label, dates, author, keywords, indexed content, or
description. Only objects that match all specified search criteria will be found. For example, you
could locate all streams containing one or more clustering models that also have a specific label
applied, and which were modified after a specific date.
Figure 9-15
Searching for streams containing a specific type of model

Object Types. You can restrict the search to models, streams, outputs, nodes, SuperNodes, projects,
model palettes, scenarios, or other types of objects.

Models. You can search for models by category (classification, approximation, clustering,
etc.) or by a specific modeling algorithm, such as Kohonen.

You can also search by fields used—for example, all models that use a field named income
as an input or output (target) field.

Streams. For streams, you can restrict the search by fields used, or model type (either category
or algorithm) contained in the stream.

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Topics. You can search on models associated with specific topics from a list set by repository
users with the appropriate privileges (for more information, see the Deployment Manager User’s
Guide). To obtain the list, check this box, then click the Add Topics button that is displayed, select
one or more topics from the list and click OK.
Label. Restricts the search to specific object version labels.
Dates. You can specify a creation or modification date and search on objects before, after, or
between the specified date range.
Author. Restricts the search to objects created by a specific user.
Keywords. Search on specific keywords. In SPSS Modeler, keywords are specified on the
Annotation tab for a stream, model, or output object.
Description. Search on specific terms in the description field. In SPSS Modeler, the description is
specified on the Annotation tab for a stream, model, or output object. Multiple search phrases
can be separated by semicolons—for example, income; crop type; claim value. (Note that within
a search phrase, spaces matter. For example, crop type with one space and crop type with two
spaces are not the same.)

Modifying Repository Objects
You can modify existing objects in the repository directly from SPSS Modeler. You can:

Create, rename, or delete folders

Lock or unlock objects

Delete objects

Creating, Renaming, and Deleting Folders
E To perform operations on folders in the repository, on the SPSS Modeler main menu click:
Tools > Repository > Explore...
E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository on p. 161. For specific port, password, and other connection
details, contact your local system administrator.
E Ensure that the Folders tab is active.
E To create a new folder, right-click the parent folder and click New Folder.
E To rename a folder, right-click it and click Rename Folder.
E To delete a folder, right-click it and click Delete Folder.

Locking and Unlocking Repository Objects
You can lock an object to prevent other users from updating any of its existing versions or creating
new versions. A locked object is indicated by a padlock symbol over the object icon.

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Figure 9-16
Locked object

To lock an object
E In the repository explorer window, right-click the required object.
E Click Lock.

To unlock an object
E In the repository explorer window, right-click the required object.
E Click Unlock.

Deleting Repository Objects
Before deleting an object from the repository, you must decide if you want to delete all versions of
the object, or just a particular version.
To Delete All Versions of an Object
E In the repository explorer window, right-click the required object.
E Click Delete Objects.

To Delete the Most Recent Version of an Object
E In the repository explorer window, right-click the required object.
E Click Delete.

To Delete a Previous Version of an Object
E In the repository explorer window, right-click the required object.
E Click Delete Versions.
E Select the version(s) to delete and click OK.

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Figure 9-17
Select versions to delete

Managing Properties of Repository Objects
You can control various object properties from SPSS Modeler. You can:

View the properties of a folder

View and edit the properties of an object

Create, apply and delete version labels for an object

Viewing Folder Properties
To view properties for any folder in the repository window, right-click the required folder. Click
Folder Properties.
General tab
Figure 9-18
Folder properties

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Displays the folder name, creation, and modification dates.
Permissions tab

Specifies read and write permissions for the folder. All users and groups with access to the
parent folder are listed. Permissions follow a hierarchy. For example, if you do not have read
permission, you cannot have write permission. If you do not have write permission, you cannot
have delete permission.
Figure 9-19
Folder properties

Users And Groups. Lists the repository users and groups that have at least Read access to this
folder. Select the Write and Delete check boxes to add those access rights for this folder to a
particular user or group. Click the Add Users/Groups icon on the right side of the Permissions
tab to assign access to additional users and groups. The list of available users and groups is
controlled by the administrator.
Cascade Permissions. Choose an option to control how changes made to the current folder are
applied to its child folders, if any.

Cascade all permissions. Cascades permission settings from the current folder to all child and
descendant folders. This is a quick way to set permissions for several folders at once. Set
permissions as required for the parent folder, and then cascade as required.

Cascade changes only. Cascades only changes made since the last time changes were applied.
For example, if a new group has been added and you want to give it access to all folders under
the Sales branch, you can give the group access to the root Sales folder and cascade the
change to all subfolders. All other permissions to existing subfolders remain as before.

Do not cascade. Any changes made apply to the current folder only and do not cascade
to child folders.

Viewing and Editing Object Properties
In the Object Properties dialog box you can view and edit properties. Although some properties
cannot be changed, you can always update an object by adding a new version.

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E In the repository window, right-click the required object.
E Click Object Properties.
Figure 9-20
Object properties

General Tab
Name. The name of the object as viewed in the repository.
Created on. Date the object (not the version) was created.
Last modified. Date the most recent version was modified.
Author. The user’s login name.
Description. By default, this contains the description specified on the object’s Annotation tab in
SPSS Modeler.
Linked topics. The repository allows models and related objects to be organized by topics if
required. The list of available topics is set by repository users with the appropriate privileges (for
more information, see the Deployment Manager User’s Guide).
Keywords. You specify keywords on the Annotation tab for a stream, model, or output object.
Multiple keywords should be separated by spaces, up to a maximum of 255 characters. (If
keywords contain spaces, use quotation marks to separate them.)
Versions Tab

Objects stored in the repository may have multiple versions. The Versions tab displays information
about each version.

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Figure 9-21
Version properties

The following properties can be specified or modified for specific versions of a stored object:
Version. Unique identifier for the version generated based on the time when the version was stored.
Label. Current label for the version, if any. Unlike the version identifier, labels can be moved from
one version of an object to another.

The file size, creation date, and author are also displayed for each version.
Edit Labels. Click the Edit Labels icon at the top right of the Versions tab to define, apply or
remove labels for stored objects. For more information, see the topic Managing Object Version
Labels on p. 183.
Permissions Tab

The Permissions tab lets you set read and write permissions for the object. All users and groups
with access to the current object are listed. Permissions follow a hierarchy. For example, if
you do not have read permission, you cannot have write permission. If you do not have write
permission, you cannot have delete permission.

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Figure 9-22
Object access rights

Users And Groups. Lists the repository users and groups that have at least Read access to this
object. Select the Write and Delete check boxes to add those access rights for this object to a
particular user or group. Click the Add Users/Groups icon on the right side of the Permissions
tab to assign access to additional users and groups. The list of available users and groups is
controlled by the administrator.

Managing Object Version Labels
The Edit Version Labels dialog box enables you to:

Apply labels to the selected object

Remove labels from the selected object

Define a new label and apply it to the object

To apply labels to the object
E Select one or more labels in the Available Labels list.
E Click the right-arrow button to move the selected labels to the Applied Labels list.
E Click OK.

To remove labels from the object
E Select one or more labels in the Applied Labels list.
E Click the left-arrow button to move the selected labels to the Available Labels list.
E Click OK.

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To define a new label and apply it to the object
E Type the label name in the New Label field.
E Click the right-arrow button to move the new label to the Applied Labels list.
E Click OK.

Deploying Streams
To enable a stream to be used with the thin-client application IBM® SPSS® Modeler Advantage,
it must be deployed as a stream (.str file) in the repository.
Whether a stream is deployed as a stream (.str file) or as a scenario (.scn file), the object can take
full advantage of the enterprise-level features of IBM® SPSS® Collaboration and Deployment
Services. For more information, see the topic Storing and Deploying Repository Objects on p. 160.
To deploy the current stream (File menu method)
E On the main menu, click:
File > Store > Deploy
E Choose the deployment type and complete the rest of the dialog box as necessary.
E Click Deploy as stream to deploy the stream for use with IBM SPSS Modeler Advantage or IBM
SPSS Collaboration and Deployment Services. Click Deploy as scenario to deploy the stream for

use with IBM SPSS Collaboration and Deployment Services or Predictive Applications version 5.
E Click Store. For more information, click Help.
E Continue from “Completing the deployment process”.

To deploy the current stream (Tools menu method)
E On the main menu, click:
Tools > Stream Properties > Deployment
E Choose the deployment type, complete the rest of the Deployment tab as necessary, and click
Store. For more information, see the topic Stream Deployment Options on p. 185.

Completing the deployment process
E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository on p. 161. For specific port, password, and other connection
details, contact your local system administrator.

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Figure 9-23
Storing a stream in the repository

E In the Repository: Store dialog box, choose the folder where you want to store the object, specify
any other information you want to record, and click the Store button. For more information, see

the topic Setting Object Properties on p. 164.

Stream Deployment Options
The Deployment tab in the Stream Options dialog box allows you to specify options for deploying
the stream. You can deploy either as a stream or as a scenario.
When you deploy as a stream, you can open and modify the stream in the thin-client application
IBM® SPSS® Modeler Advantage. The stream is stored in the repository as a file with the
extension .str.
Deploying as a scenario stores the stream in the repository as a file with the extension .scn. Doing
so also enables a stream to be used by Predictive Applications version 5.
Whether you deploy as a stream or as a scenario, you can take advantage of the additional
functionality available with IBM® SPSS® Collaboration and Deployment Services, such as
multi-user access, automated scoring, model refresh, and Champion Challenger analysis.
From the Deployment tab, you can also preview the stream description that IBM® SPSS®
Modeler creates for the stream. For more information, see the topic Stream Descriptions in
Chapter 5 on p. 74.
Note: To ensure consistent access to enterprise data, a stream that is deployed as a scenario must
access its source data through IBM® SPSS® Collaboration and Deployment Services Enterprise
View, so in such a case the stream must include at least one Enterprise View source node within
each designated scoring or modeling branch as applicable.

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Figure 9-24
Stream Deployment options

Deployment type. Choose how you want to deploy the stream. All streams require a designated
scoring node before they can be deployed; additional requirements and options depend on the
deployment type.

. The stream will not be deployed to the repository. All options are disabled except

stream description preview.

Scoring Only. The stream is deployed to the repository when you click the Store button. Data
can be scored using the node that you designate in the Scoring node field.

Model Refresh. Same as for Scoring Only but in addition, the model can be updated in the
repository using the objects that you designate in the Modeling node and Model nugget fields.

Note: Automatic model refresh is not supported by default in IBM SPSS Collaboration and
Deployment Services, so you must choose this deployment type if you want to use this feature
when running a stream from the repository. For more information, see the topic Model
Refresh on p. 190.

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Scoring node. Select a graph, output or export node to identify the stream branch to be used for
scoring the data. While the stream can actually contain any number of valid branches, models, and
terminal nodes, one and only one scoring branch must be designated for purposes of deployment.
This is the most basic requirement to deploy any stream.
Scoring Parameters. Allows you to specify parameters that can be modified when the scoring
branch is run. For more information, see the topic Scoring and Modeling Parameters on p. 188.
Modeling node. For model refresh, specifies the modeling node used to regenerate or update the
model in the repository. Must be a modeling node of the same type as that specified for Model
nugget.
Model Build Parameters. Allows you to specify parameters that can be modified when the modeling
node is run. For more information, see the topic Scoring and Modeling Parameters on p. 188.
Model nugget. For model refresh, specifies the model nugget that will be updated or regenerated
each time the stream is updated in the repository (typically as part of a scheduled job). The model
must be located on the scoring branch. While multiple models may exist on the scoring branch,
only one can be designated. Note that when the stream is initially created this may effectively be a
placeholder model that is updated or regenerated as new data is available.
Deploy as stream. Click this option if you want to use the stream with IBM SPSS Modeler
Advantage or IBM SPSS Collaboration and Deployment Services (and see note following).
Deploy as scenario. Click this option if you want to use the stream with IBM SPSS Collaboration
and Deployment Services or Predictive Applications version 5 (and see note following).
Check. Click this button to check whether this is a valid stream for deployment. All streams must
have a designated scoring node before they can be deployed. If you are deploying as a scenario,
the stream must also contain a valid Enterprise View source node. Error messages are displayed
if these conditions are not satisfied.
Store. Deploys the stream if it is valid. If not, an error message is displayed. Click the Fix button,
correct the error and try again.
Preview Stream Description. Enables you to view the contents of the stream description that SPSS
Modeler creates for the stream. For more information, see the topic Stream Descriptions in
Chapter 5 on p. 74.

Note: (Deploy as stream or scenario) Multiple Enterprise View nodes can be used within the
modeling branch. If so, using a single data connection for all Enterprise View nodes within the
branch is preferable in most cases, and is required for Champion Challenger analysis.

If Champion Challenger support is not required, different Enterprise View connections can
be used within the same branch, as long as the connections vary by data provider definition
(DPD) only.

These limitations apply within a given branch only. Between the scoring and model building
branches, different Enterprise View connections can be used without such restrictions.

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Scoring and Modeling Parameters
When deploying a stream to IBM SPSS Collaboration and Deployment Services, you can choose
which parameters can be viewed or edited each time the model is updated or scored. For example,
you might specify maximum and minimum values, or some other value that may be subject to
change each time a job is run.
Figure 9-25
Scoring Parameters dialog box

E To make a parameter visible so it can be viewed or edited after the stream is deployed, select it

from the list in the dialog box.
The list of available parameters is defined on the Parameters tab in the stream properties dialog
box. For more information, see the topic Setting Stream and Session Parameters in Chapter 5
on p. 68.

The Scoring Branch
If you are deploying a stream, one branch of the stream must be designated as the scoring branch
(that is, the one containing the scoring node). When you designate a branch as the scoring
branch, that branch is highlighted on the stream canvas, as is the model link to the nugget on the
scoring branch. This visual representation is particularly useful in complex streams with multiple
branches, where the scoring branch might not be immediately obvious.
Note: Only one stream branch can be designated as the scoring branch.

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Figure 9-26
Stream with scoring branch highlighted

If the stream already had a scoring branch defined, the newly-designated branch replaces it as the
scoring branch. You can set the color of the scoring branch indication by means of a Custom Color
option. For more information, see the topic Setting Display Options in Chapter 12 on p. 220.
You can show or hide the scoring branch indication by means of the Show/hide stream markup
toolbar button.
Figure 9-27
Show/hide stream markup toolbar button

Identifying the Scoring Branch for Deployment
You can designate the scoring branch either from the pop-up menu of a terminal node, or from the
Tools menu. If you use the pop-up menu, the scoring node is set automatically in the Deployment
tab of the stream properties.
To designate a branch as the scoring branch (pop-up menu)
E Connect the model nugget to a terminal node (a processing or output node downstream from

the nugget).
E Right-click the terminal node.
E On the menu, click Use as Scoring Branch.

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To designate a branch as the scoring branch (Tools menu)
E Connect the model nugget to a terminal node (a processing or output node downstream from

the nugget).
E On the main menu, click:
Tools > Stream Properties > Deployment
E On the Deployment type list, click Scoring Only or Model Refresh as required. For more information,

see the topic Stream Deployment Options on p. 185.
E Click the Scoring node field and select a terminal node from the list.
E Click OK.

Model Refresh
Model refresh is the process of rebuilding an existing model in a stream using newer data.
The stream itself does not change in the repository. For example, the algorithm type and
stream-specific settings remain the same, but the model is retrained on new data, and updated
if the new version of the model works better than the old one.
Only one model nugget in a stream can be set to refresh—this is known as the refresh model. If
you click the Model Refresh option on the Deployment tab of the stream properties (see Stream
Deployment Options on p. 185), the model nugget that you designate at that time becomes the
refresh model. You can also designate a model as the refresh model from the pop-up menu of a
model nugget. The nugget must already be on the scoring branch for this to be possible.
If you turn off the “refresh model” status of a nugget, this is equivalent to setting the deployment
type of the stream to Scoring Only, and the Deployment tab of the stream properties dialog box is
updated accordingly. You can turn this status on and off by means of the Use as Refresh Model
option on the pop-up menu of the nugget on the current scoring branch.
Removing the model link of a nugget on the scoring branch also removes the “refresh model”
status of the nugget. You can undo removal of the model link by means of the Edit menu or the
toolbar; doing so also reinstates the “refresh model” status of the nugget.

How the Refresh Model is Selected
As well as the scoring branch, the link to the refresh model is also highlighted in the stream. The
model nugget chosen as the refresh model, and therefore the link that is highlighted, depends on
how many nuggets are in the stream.

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Single Model in Stream
Figure 9-28
Scoring branch with single model in the stream

If a single linked model nugget is on the scoring branch when it is identified as such, that nugget
becomes the refresh model for the stream.
Multiple Models in Stream

If there is more than one linked nugget in the stream, the refresh model is chosen as follows.
If a model nugget has been defined in the Deployment tab of the stream properties dialog box and
is also in the stream, then that nugget becomes the refresh model.
If no nugget has been defined in the Deployment tab, or if one has been defined but is not on the
scoring branch, then the nugget closest to the terminal node becomes the refresh model.
To illustrate this, suppose that you have the following stream.

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Figure 9-29
Scoring branch with more than one model in the stream

You right-click the Analysis node and use its menu to set the scoring branch, which is now
highlighted. Doing so also designates the model closest to the Analysis node as the refresh model,
as indicated by the highlighted refresh link.
Figure 9-30
Scoring branch highlighted with multiple models and refresh link

However, you decide that you want to use the other model nugget in the stream as the refresh
model, so from its menu, you set its model link to be used as the refresh link.

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Figure 9-31
Scoring branch with refresh link switched to first model nugget

If you subsequently deselect both model links as refresh links, only the scoring branch is
highlighted, not the links. The deployment type is set to Scoring Only.
Figure 9-32
Scoring branch with multiple models and no refresh links

Note: You can choose to set one of the links to Replace status, but not the other one. In this case,
the model nugget chosen as the refresh model is the one that has a refresh link and which is closest
to the terminal node when the scoring branch is designated.

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No Models in Stream

If there are no models in the stream, or only models with no model links, the deployment type is
set to Scoring Only.

Checking a Scoring Branch for Errors
When you designate the scoring branch, it is checked for errors, such as not having an Enterprise
View node in the stream when deploying as a scenario.
Figure 9-33
Scoring branch with errors

If an error is found, the scoring branch is highlighted in the scoring branch error color, and an
error message is displayed. You can set the error color by means of a Custom Color option. For
more information, see the topic Setting Display Options in Chapter 12 on p. 220.
If an error is found, proceed as follows:
E Correct the error according to the contents of the error message.
E Do one of the following:

Right-click the terminal node and click Check Scenario on the pop-up menu.

On the main menu, click:

Tools > Stream Properties > Deployment

and click Check.
E If necessary, repeat this process until no errors are found.

Chapter

Exporting to External Applications

About Exporting to External Applications
IBM® SPSS® Modeler provides a number of mechanisms to export the entire data mining process
to external applications, so that the work you do to prepare data and build models can be used to
your advantage outside of SPSS Modeler as well.
The previous section showed how you can deploy streams to an IBM SPSS Collaboration and
Deployment Services repository to take advantage of its multi-user access, job scheduling and
other features. In a similar way, SPSS Modeler streams can also be used in conjunction with:

IBM® SPSS® Modeler Advantage

Predictive Applications 5.0 applications

Applications that can import and export files in PMML format

For more information about using streams with IBM SPSS Modeler Advantage, see Opening a
Stream in IBM SPSS Modeler Advantage on p. 195.
To export a stream for use with Predictive Applications 5.0, follow the instructions for deploying
as a scenario. For more information, see the topic Deploying Streams in Chapter 9 on p. 184.
For information on exporting and importing models as PMML files, making it possible to share
models with any other applications that support this format, see Importing and Exporting Models
as PMML on p. 196.
Note: The Predictive Applications product has been superseded by IBM® Analytical Decision
Management. Support for Predictive Applications will be withdrawn in a future release of SPSS
Modeler.

Opening a Stream in IBM SPSS Modeler Advantage
IBM® SPSS® Modeler streams can be used in conjunction with the thin-client application IBM®
SPSS® Modeler Advantage. While it is possible to create customized applications entirely within
IBM SPSS Modeler Advantage, you can also use a stream already created in SPSS Modeler as
the basis of an application workflow.
To open a stream in IBM SPSS Modeler Advantage:
E Deploy the stream in the IBM® SPSS® Collaboration and Deployment Services repository,
being sure to click the Deploy as stream option. For more information, see the topic Deploying

Streams in Chapter 9 on p. 184.
E Click the Open in IBM SPSS Modeler Advantage toolbar button, or from the main menu click:
File > Open in IBM SPSS Modeler Advantage
© Copyright IBM Corporation 1994, 2012.

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E Specify connection settings to the repository if necessary. For more information, see the

topic Connecting to the Repository in Chapter 9 on p. 161. For specific port, password, and other
connection details, contact your local system administrator.
Note: The repository server must also have the IBM SPSS Modeler Advantage software installed.
E In the Repository: Store dialog box, choose the folder where you want to store the object, specify
any other information you want to record, and click the Store button. For more information, see

the topic Setting Object Properties in Chapter 9 on p. 164.
Doing so launches IBM SPSS Modeler Advantage with the stream already open. The stream
is closed in SPSS Modeler.

Importing and Exporting Models as PMML
PMML, or predictive model markup language, is an XML format for describing data mining
and statistical models, including inputs to the models, transformations used to prepare data for
data mining, and the parameters that define the models themselves. IBM® SPSS® Modeler can
import and export PMML, making it possible to share models with other applications that support
this format, such as IBM® SPSS® Statistics.
For more information about PMML, see the Data Mining Group website (http://www.dmg.org).
To Export a Model

PMML export is supported for most of the model types generated in SPSS Modeler. For more
information, see the topic Model Types Supporting PMML on p. 198.
E Right-click a model nugget on the models palette. (Alternatively, double-click a model nugget on

the canvas and select the File menu.)
E On the menu, click Export PMML.
Figure 10-1
Exporting a model in PMML format

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E In the Export (or Save) dialog box, specify a target directory and a unique name for the model.

Note: You can change options for PMML export in the User Options dialog box. On the main
menu, click:
Tools > Options > User Options

and click the PMML tab.
For more information, see the topic Setting PMML Export Options in Chapter 12 on p. 221.
To Import a Model Saved as PMML

Models exported as PMML from SPSS Modeler or another application can be imported into the
models palette. For more information, see the topic Model Types Supporting PMML on p. 198.
E In the models palette, right-click the palette and select Import PMML from the menu.
Figure 10-2
Importing a model in PMML format

E Select the file to import and specify options for variable labels as required.
E Click Open.

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Figure 10-3
Selecting the XML file for a model saved using PMML

Use variable labels if present in model. The PMML may specify both variable names and variable
labels (such as Referrer ID for RefID) for variables in the data dictionary. Select this option to use
variable labels if they are present in the originally exported PMML.

If you have selected the variable label option but there are no variable labels in the PMML, the
variable names are used as normal.

Model Types Supporting PMML
PMML Export
SPSS Modeler models. The following models created in IBM® SPSS® Modeler can be exported
as PMML 4.0:

C&R Tree

QUEST

CHAID

Linear Regression

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Neural Net

C5.0

Logistic Regression

Genlin

SVM

Bayes Net

Apriori

Carma

K-Means

Kohonen

TwoStep

KNN

Statistics Model

The following model created in SPSS Modeler can be exported as PMML 3.2:

Decision List

Database native models. For models generated using database-native algorithms, PMML export is
available for IBM InfoSphere Warehouse models only. Models created using Analysis Services
from Microsoft or Oracle Data Miner cannot be exported. Also note that IBM models exported as
PMML cannot be imported back into SPSS Modeler.
PMML Import

SPSS Modeler can import and score PMML models generated by current versions of all IBM®
SPSS® Statistics products, including models exported from SPSS Modeler as well as model or
transformation PMML generated by SPSS Statistics 17.0 or later. Essentially, this means any
PMML that the scoring engine can score, with the following exceptions:

Apriori, CARMA, Anomaly Detection, and Sequence models cannot be imported.

PMML models may not be browsed after importing into SPSS Modeler even though they can
be used in scoring. (Note that this includes models that were exported from SPSS Modeler
to begin with. To avoid this limitation, export the model as a generated model file [*.gm]
rather than PMML.)

IBM InfoSphere Warehouse models exported as PMML cannot be imported.

Limited validation occurs on import, but full validation is performed on attempting to score the
model. Thus it is possible for import to succeed but scoring to fail or produce incorrect results.

Chapter

Projects and Reports

Introduction to Projects
A project is a group of files related to a data mining task. Projects include data streams, graphs,
generated models, reports, and anything else that you have created in IBM® SPSS® Modeler. At
first glance, it may seem that SPSS Modeler projects are simply a way to organize output, but they
are actually capable of much more. Using projects, you can:

Annotate each object in the project file.

Use the CRISP-DM methodology to guide your data mining efforts. Projects also contain
a CRISP-DM Help system that provides details and real-world examples on data mining
with CRISP-DM.

Add non-SPSS Modeler objects to the project, such as a PowerPoint slide show used to present
your data mining goals or white papers on the algorithms that you plan to use.

Produce both comprehensive and simple update reports based on your annotations. These
reports can be generated in HTML for easy publishing on your organization’s intranet.

Note: If the project pane is not visible in the SPSS Modeler window, click Project on the View
menu.
Objects that you add to a project can be viewed in two ways: Classes view and CRISP-DM view.
Anything that you add to a project is added to both views, and you can toggle between views to
create the organization that works best.
Figure 11-1
CRISP-DM view and Classes view of a project file

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CRISP-DM View
By supporting the Cross-Industry Standard Process for Data Mining (CRISP-DM), IBM® SPSS®
Modeler projects provide an industry-proven and non-proprietary way of organizing the pieces
of your data mining efforts. CRISP-DM uses six phases to describe the process from start
(gathering business requirements) to finish (deploying your results). Even though some phases
do not typically involve work in SPSS Modeler, the project pane includes all six phases so that
you have a central location for storing and tracking all materials associated with the project.
For example, the Business Understanding phase typically involves gathering requirements and
meeting with colleagues to determine goals rather than working with data in SPSS Modeler. The
project pane allows you to store your notes from such meetings in the Business Understanding
folder for future reference and inclusion in reports.
Figure 11-2
CRISP-DM view

The CRISP-DM view in the project pane is also equipped with its own Help system to guide you
through the data mining life cycle. From SPSS Modeler, this help can be accessed by clicking
CRISP-DM Help on the Help menu.
Note: If the project pane is not visible in the window, click Project on the View menu.

Setting the Default Project Phase
Objects added to a project are added to a default phase of CRISP-DM. This means that you need
to organize objects manually according to the data mining phase in which you used them. It is
wise to set the default folder to the phase in which you are currently working.
To select which phase to use as your default:
E In CRISP-DM view, right-click the folder for the phase to set as the default.
E On the menu, click Set as Default.

The default folder is displayed in bold type.

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Classes View
The Classes view in the project pane organizes your work in IBM® SPSS® Modeler categorically
by the types of objects created. Saved objects can be added to any of the following categories:

Streams

Nodes

Models

Tables, graphs, reports

Other (non-SPSS Modeler files, such as slide shows or white papers relevant to your data
mining work)

Figure 11-3
Classes view

Adding objects to the Classes view also adds them to the default phase folder in the CRISP-DM
view.
Note: If the project pane is not visible in the window, click Project on the View menu.

Building a Project
A project is essentially a file containing references to all of the files that you associate with the
project. This means that project items are saved both individually and as a reference in the project
file (.cpj). Because of this referential structure, note the following:

Project items must first be saved individually before being added to a project. If an item is
unsaved, you will be prompted to save it before adding it to the current project.

Objects that are updated individually, such as streams, are also updated in the project file.

Manually moving or deleting objects (such as streams, nodes, and output objects) from the file
system will render links in the project file invalid.

Creating a New Project
New projects are easy to create in the IBM® SPSS® Modeler window. You can either start
building one, if none is open, or you can close an existing project and start from scratch.

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E On the main menu, click:
File > Project > New Project...

Adding to a Project
Once you have created or opened a project, you can add objects, such as data streams, nodes,
and reports, using several methods.
Adding Objects from the Managers

Using the managers in the upper right corner of the IBM® SPSS® Modeler window, you can
add streams or output.
E Select an object, such as a table or a stream, from one of the manager tabs.
E Right-click, and click Add to Project.

If the object has been previously saved, it will automatically be added to the appropriate objects
folder (in Classes view) or to the default phase folder (in CRISP-DM view).
E Alternatively, you can drag and drop objects from the managers to the project pane.

Note: You may be asked to save the object first. When saving, be sure that Add file to project is
selected in the Save dialog box. This will automatically add the object to the project after you
save it.
Figure 11-4
Adding items to a project

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Adding Nodes from the Canvas

You can add individual nodes from the stream canvas by using the Save dialog box.
E Select a node on the canvas.
E Right-click, and click Save Node. Alternatively, on the main menu click:
Edit > Node > Save Node...
E In the Save dialog box, select Add file to project.
E Create a name for the node and click Save.

This saves the file and adds it to the project. Nodes are added to the Nodes folder in Classes view
and to the default phase folder in CRISP-DM view.
Adding External Files

You can add a wide variety of non-SPSS Modeler objects to a project. This is useful when you
are managing the entire data mining process within SPSS Modeler. For example, you can store
links to data, notes, presentations, and graphics in a project. In CRISP-DM view, external files
can be added to the folder of your choice. In Classes view, external files can be saved only to
the Other folder.
To add external files to a project:
E Drag files from the desktop to the project.

or
E Right-click the target folder in CRISP-DM or Classes view.
E On the menu, click Add to Folder.
E Select a file in the dialog box and click Open.

This will add a reference to the selected object inside SPSS Modeler projects.

Transferring Projects to the IBM SPSS Collaboration and Deployment Services
Repository
You can transfer an entire project, including all component files, to the IBM® SPSS®
Collaboration and Deployment Services Repository in one step. Any objects that are already in
the target location will not be moved. This feature also works in reverse: you can transfer entire
projects from the IBM SPSS Collaboration and Deployment Services Repository to your local
file system.
Note: A separate license is required to access an IBM® SPSS®
Collaboration and Deployment Services repository. For more information, see
http://www.ibm.com/software/analytics/spss/products/deployment/cds/

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Transferring a Project

Make sure that the project you want to transfer is open in the project pane.
To transfer a project:
E Right-click the root project folder and click Transfer Project.
E If prompted, log in to IBM SPSS Collaboration and Deployment Services Repository.
E Specify the new location for the project and click OK.

Setting Project Properties
You can customize a project’s contents and documentation by using the project properties dialog
box. To access project properties:
E Right-click an object or folder in the project pane and click Project Properties.
E Click the Project tab to specify basic project information.
Figure 11-5
Setting project properties

Created. Shows the project’s creation date (not editable).

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Summary. You can enter a summary for your data mining project that will be displayed in the
project report.
Contents. Lists the type and number of components referenced by the project file (not editable).
Save unsaved object as. Specifies whether unsaved objects should be saved to the local file system,
or stored in the repository. For more information, see the topic About the IBM SPSS Collaboration
and Deployment Services Repository in Chapter 9 on p. 158.
Update object references when loading project. Select this option to update the project’s references
to its components. Note: The files added to a project are not saved in the project file itself. Rather,
a reference to the files is stored in the project. This means that moving or deleting a file will
remove that object from the project.

Annotating a Project
The project pane provides a number of ways to annotate your data mining efforts. Project-level
annotations are often used to track “big-picture” goals and decisions, while folder or node
annotations provide additional detail. The Annotations tab provides enough space for you to
document project-level details, such as the exclusion of data with irretrievable missing data or
promising hypotheses formed during data exploration.
To annotate a project:
E Select the project folder in either CRISP-DM or Classes view.
E Right-click the folder and click Project Properties.
E Click the Annotations tab.

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Figure 11-6
Annotations tab in the project properties dialog box

E Enter keywords and text to describe the project.

Folder Properties and Annotations
Individual project folders (in both CRISP-DM and Classes view) can be annotated. In CRISP-DM
view, this can be an extremely effective way to document your organization’s goals for each phase
of data mining. For example, using the annotation tool for the Business Understanding folder,
you can include documentation such as “The business objective for this study is to reduce churn
among high-value customers.” This text could then be automatically included in the project
report by selecting the Include in report option.
To annotate a folder:
E Select a folder in the project pane.
E Right-click the folder and click Folder Properties.

In CRISP-DM view, folders are annotated with a summary of the purpose of each phase as well
as guidance on completing the relevant data mining tasks. You can remove or edit any of these
annotations.

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Figure 11-7
Project folder with CRISP-DM annotation

Name. This area displays the name of the selected field.
Tooltip text. Create custom ToolTips that will be displayed when you hover the mouse pointer over
a project folder. This is useful in CRISP-DM view, for example, to provide a quick overview of
each phase’s goals or to mark the status of a phase, such as “In progress” or “Complete.”
Annotation field. Use this field for more lengthy annotations that can be collated in the project
report. The CRISP-DM view includes a description of each data mining phase in the annotation,
but you should feel free to customize this for your own project.
Include in report. To include the annotation in reports, select Include in report.

Object Properties
You can view object properties and choose whether to include individual objects in the project
report. To access object properties:
E Right-click an object in the project pane.
E On the menu, click Object Properties.
Figure 11-8
Object properties dialog box

Name. This area lists the name of the saved object.
Path. This area lists the location of the saved object.
Include in report. Select this option to include the object details in a generated report.

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Closing a Project
When you exit IBM® SPSS® Modeler or open a new project, the existing project file (.cpj)
is closed.
Some files associated with the project (such as streams, nodes or graphs) may still be open. If you
want to leave these files open, reply No to the message ... Do you want to save and close these files?
If you modify and save any associated files after the close of a project, these updated versions will
be included in the project the next time you open it. To prevent this behavior, remove the file from
the project or save it under a different filename.

Generating a Report
One of the most useful features of projects is the ability to generate reports based on the project
items and annotations. This is a critical component of effective data mining, as discussed
throughout the CRISP-DM methodology. You can generate a report directly into one of several
file types or to an output window on the screen for immediate viewing. From there, you can
print, save, or view the report in a web browser. You can distribute saved reports to others
in your organization.
Reports are often generated from project files several times during the data mining process
for distribution to those involved in the project. The report culls information about the objects
referenced from the project file as well as any annotations created. You can create reports based
on either the Classes view or CRISP-DM view.

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Figure 11-9
Generated report window

To generate a report:
E Select the project folder in either CRISP-DM or Classes view.
E Right-click the folder and click Project Report.
E Specify the report options and click Generate Report.

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Figure 11-10
Selecting options for a report

The options in the report dialog box provide several ways to generate the type of report you need:
Output name. Specify the name of the output window if you choose to send the output of the
report to the screen. You can specify a custom name or let IBM® SPSS® Modeler automatically
name the window for you.
Output to screen. Select this option to generate and display the report in an output window. Note
that you have the option to export the report to various file types from the output window.
Output to file. Select this option to generate and save the report as a file of the type specified
in the File type list.
Filename. Specify a filename for the generated report. Files are saved by default to the SPSS
Modeler \bin directory. Use the ellipsis button (...) to specify a different location.

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File type. Available file types are:

HTML document. The report is saved as a single HTML file. If your report contains graphs,
they are saved as PNG files and are referenced by the HTML file. When publishing your
report on the Internet, make sure to upload both the HTML file and any images it references.

Text document. The report is saved as a single text file. If your report contains graphs, only
the filename and path references are included in the report.

Microsoft Word document. The report is saved as a single document, with any graphs
embedded directly into the document.

Microsoft Excel document. The report is saved as a single spreadsheet, with any graphs
embedded directly into the spreadsheet.

Microsoft PowerPoint document. Each phase is shown on a new slide. Any graphs are
embedded directly into the PowerPoint slides.

Output object. When opened in SPSS Modeler, this file (.cou) is the same as the Output to
screen option in the Report Format group.

Note: To export to a Microsoft Office file, you must have the corresponding application installed.
Title. Specify a title for the report.
Report structure. Select either CRISP-DM or Classes. CRISP-DM view provides a status report
with “big-picture” synopses as well as details about each phase of data mining. Classes view is an
object-based view that is more appropriate for internal tracking of data and streams.
Author. The default user name is displayed, but you can change it.
Report includes. Select a method for including objects in the report. Select all folders and objects to
include all items added to the project file. You can also include items based on whether Include in
Report is selected in the object properties. Alternatively, to check on unreported items, you can
choose to include only items marked for exclusion (where Include in Report is not selected).
Select. This option allows you to provide project updates by selecting only recent items in the
report. Alternatively, you can track older and perhaps unresolved issues by setting parameters for
old items. Select all items to dismiss time as a parameter for the report.
Order by. You can select a combination of the following object characteristics to order them
within a folder:

Type. Group objects by type.

Name. Organize objects alphabetically.

Added date. Sort objects using the date they were added to the project.

Saving and Exporting Generated Reports
A report generated to the screen is displayed in a new output window. Any graphs included
in the report are displayed as in-line images.

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The total number of nodes in each stream is listed within the report. The numbers are shown
under the following headings, which use IBM® SPSS® Modeler terminology, not CRISP-DM
terminology:

Data readers. Source nodes.

Data writers. Export nodes.

Model builders. Build, or Modeling, nodes.

Model appliers. Generated models, also known as nuggets.

Output builders. Graph or Output nodes.

Other. Any other nodes related to the project. For example, those available on the Field Ops
tab or Record Ops tab on the Nodes Palette.

To save a report:
E On the File menu, click Save.
E Specify a filename.

The report is saved as an output object.
To export a report:
E On the File menu, click Export and the file type to which you want to export.
E Specify a filename.

The report is saved in the format you chose.
You can export to the following file types:

HTML

Text

Microsoft Word

Microsoft Excel

Microsoft PowerPoint

Note: To export to a Microsoft Office file, you must have the corresponding application installed.
Use the buttons at the top of the window to:

Print the report.

View the report as HTML in an external web browser.

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Figure 11-11
Report displayed in a web browser

Chapter

Customizing IBM SPSS Modeler

Customizing IBM SPSS Modeler Options
There are a number of operations you can perform to customize IBM® SPSS® Modeler to your
needs. Primarily, this customization consists of setting specific user options such as memory
allocation, default directories, and use of sound and color. You can also customize the Nodes
palette located at the bottom of the SPSS Modeler window.

Setting IBM SPSS Modeler Options
There are several ways to customize and set options for IBM® SPSS® Modeler:

Set system options, such as memory usage and locale, by clicking System Options on the
Tools > Options menu.

Set user options, such as display fonts and colors, by clicking User Options on the Tools >
Options menu.

Specify the location of applications that work with SPSS Modeler by clicking Helper
Applications on the Tools > Options menu.

Specify the default directories used in SPSS Modeler by clicking Set Directory or Set Server
Directory on the File menu.

You can also set options that apply to some or all of your streams. For more information, see the
topic Setting Options for Streams in Chapter 5 on p. 54.

System Options
You can specify the preferred language or locale for IBM® SPSS® Modeler by clicking System
Options on the Tools > Options menu. Here you can also set the maximum memory usage for
SPSS Modeler. Note that changes made in this dialog box will not take effect until you restart
SPSS Modeler.

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Figure 12-1
System Options dialog box

Maximum memory. Select to impose a limit in megabytes on SPSS Modeler’s memory usage. On
some platforms, SPSS Modeler limits its process size to reduce the toll on computers with limited
resources or heavy loads. If you are dealing with large amounts of data, this may cause an “out of
memory” error. You can ease memory load by specifying a new threshold.
Use system locale. This option is selected by default and set to English (United States). Deselect
to specify another language from the list of available languages and locales.

Managing Memory
In addition to the Maximum memory setting specified in the System Options dialog box, there are
several ways you can optimize memory usage:

Set up a cache on any nonterminal node so that the data is read from the cache rather than
retrieved from the data source when you run the data stream. This will help decrease the
memory load for large data sets. For more information, see the topic Caching Options for
Nodes in Chapter 5 on p. 50.

Adjust the Maximum members for nominal fields option in the stream properties dialog box.
This option specifies a maximum number of members for nominal fields after which the
measurement level of the field becomes Typeless. For more information, see the topic Setting
general options for streams in Chapter 5 on p. 55.

Force IBM® SPSS® Modeler to free up memory by clicking in the lower right corner of
the window where the memory that SPSS Modeler is using and the amount allocated are
displayed (xxMB / xxMB). Clicking this region turns it a darker shade, after which memory
allocation figures will drop. Once the region returns to its regular color, SPSS Modeler has
freed up all the memory possible.

Setting Default Directories
You can specify the default directory used for file browsers and output by selecting Set Directory or
Set Server Directory from the File menu.

Set Directory. You can use this option to set the working directory. The default working
directory is based on the installation path of your version of IBM® SPSS® Modeler or from
the command line path used to launch SPSS Modeler. In local mode, the working directory

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is the path used for all client-side operations and output files (if they are referenced with
relative paths).

Set Server Directory. The Set Server Directory option on the File menu is enabled whenever
there is a remote server connection. Use this option to specify the default directory for
all server files and data files specified for input or output. The default server directory is
$CLEO/data, where $CLEO is the directory in which the Server version of SPSS Modeler
is installed. Using the command line, you can also override this default by using the
-server_directory flag with the modelerclient command line argument.

Setting User Options
You can set general options for IBM® SPSS® Modeler by selecting User Options from the Tools >
Options menu. These options apply to all streams used in SPSS Modeler.
The following types of options can be set by clicking the corresponding tab:

Notification options, such as model overwriting and error messages.

Display options, such as graph and background colors.

PMML export options used when exporting models to Predictive Model Markup Language
(PMML).

User or author information, such as your name, initials, and e-mail address. This information
may be displayed on the Annotations tab for nodes and for other objects that you create.

To set stream-specific options, such as decimal separators, time and data formats, optimization,
stream layout, and stream scripts, use the Stream Properties dialog box, available from the File
and Tools menus.

Setting Notification Options
Using the Notifications tab of the User Options dialog box, you can set various options regarding
the occurrence and type of warnings and confirmation windows in IBM® SPSS® Modeler. You
can also specify the behavior of the Outputs and Models tabs in the managers pane when new
output and models are generated.

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Figure 12-2
User Options dialog box, Notifications tab

Show stream execution feedback dialog. Select to display a dialog box that includes a progress
indicator when a stream has been running for three seconds. The dialog box also includes details
of the output objects created by the stream.

Close dialog upon completion. By default, the dialog box closes when the stream finishes
running. Clear this check box if you want the dialog box to remain visible when the stream
finishes.

Warn when a node overwrites a file. Select to warn with an error message when node operations
overwrite an existing file.
Warn when a node overwrites a database table. Select to warn with an error message when node
operations overwrite an existing database table.
Sound Notifications

Use the list to specify whether sounds notify you when an event or error occurs. There are a
number of sounds available. Use the Play (loudspeaker) button to play a selected sound. Use the
ellipsis button (...) to browse for and select a sound.
Note: The .wav files used to create sounds in SPSS Modeler are stored in the /media/sounds
directory of your installation.

Mute all sounds. Select to turn off sound notification for all events.

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Visual Notifications

The options in this group are used to specify the behavior of the Outputs and Models tabs in the
managers pane at the top right of the display when new items are generated. Select New Model or
New Output from the list to specify the behavior of the corresponding tab.
The following options are available for New Model:
Add model to stream. If selected (default), adds a new model to the stream, as well as to the
Models tab, as soon as the model is built. In the stream, the model is shown with a link to the
modeling node from which the model was created. If you uncheck this box, the model is added
only to the Models tab.
Replace previous model. If selected (default), overwrites an existing model from this stream in
the Models tab and on the stream canvas. If this box is unchecked, the model is added to the
existing models on the tab and the canvas. Note that this setting is overridden by the model
replacement setting on a model link.

The following options are available for New Output:
Warn when outputs exceed [n]. Select whether to display a warning when the number of items
on the Outputs tab exceeds a prespecified quantity. The default quantity is 20; however, you
can change this if needed.

The following options are available in all cases:
Select tab. Choose whether to switch to the Outputs or Models tab when the corresponding object
is generated while the stream runs.

Select Always to switch to the corresponding tab in the managers pane.

Select If generated by current stream to switch to the corresponding tab only for objects
generated by the stream currently visible in the canvas.

Select Never to restrict the software from switching to the corresponding tab to notify you of
generated outputs or models.

Flash tab. Select whether to flash the Outputs or Models tab in the managers pane when new
outputs or models have been generated.

Select If not selected to flash the corresponding tab (if not already selected) whenever new
objects are generated in the managers pane.

Select Never to restrict the software from flashing the corresponding tab to notify you of
generated objects.

Scroll palette to make visible (New Model only). Select whether to automatically scroll the Models
tab in the managers pane to make the most recent model visible.

Select Always to enable scrolling.

Select If generated by current stream to scroll only for objects generated by the stream currently
visible in the canvas.

Select Never to restrict the software from automatically scrolling the Models tab.

Open window (New Output only). Select whether to automatically open an output window upon
generation.

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Select Always to always open a new output window.

Select If generated by current stream to open a new window for output generated by the stream
currently visible in the canvas.

Select Never to restrict the software from automatically opening new windows for generated
output.

Click Default Values to revert to the system default settings for this tab.

Setting Display Options
Using the Display tab of the User Options dialog box, you can set options for the display of
fonts and colors in IBM® SPSS® Modeler.
Figure 12-3
User Options dialog box, Display tab

Show welcome dialog on startup. Select to cause the welcome dialog box to be displayed on
startup. The welcome dialog box has options to launch the application examples tutorial, open a
demonstration stream or an existing stream or project, or to create a new stream.
Show stream and SuperNode markups. If selected, causes markup (if any) on streams and
SuperNodes to be displayed by default. Markup includes stream comments, model links and
scoring branch highlighting.

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Standard Fonts & Colors (effective on restart). Options in this control box are used to specify the
SPSS Modeler screen design, color scheme, and the size of the fonts displayed. Options selected
here do not take effect until you close and restart SPSS Modeler.

Look and feel. Enables you to choose a standard color scheme and screen design. You can
choose from
SPSS Standard (default), a design common across IBM SPSS products.
SPSS Classic, a design familiar to users of earlier versions of SPSS Modeler.
Windows, a Windows design that may be useful for increased contrast in the stream canvas
and palettes.

Default font size for nodes. Specify a font size to be used in the node palettes and for nodes
displayed in the stream canvas.

Note: You can set the size of the node icons for a stream on the Layout pane of the Options tab of
the stream properties dialog box. From the main menu, choose
Tools > Stream Properties > Options > Layout.

Custom Colors.This table lists the currently selected colors used for various display items. For
each of the items listed in the table, you can change the current color by double-clicking the
corresponding row in the Color column and selecting a color from the list. To specify a custom
color, scroll to the bottom of the list and click the Color... entry.
Chart Category Color Order. This table lists the currently selected colors used for display in newly
created graphs. The order of the colors reflects the order in which they will be used in the chart.
For example, if a nominal field used as a color overlay contains four unique values, then only the
first four colors listed here will be used. For each of the items listed in the table, you can change
the current color by double-clicking the corresponding row in the Color column and selecting a
color from the list. To specify a custom color, scroll to the bottom of the list and click the Color...
entry. Changes made here do not affect previously created graphs.

Click Default Values to revert to the system default settings for this tab.

Setting PMML Export Options
On the PMML tab, you can control how IBM® SPSS® Modeler exports models to Predictive
Model Markup Language (PMML). For more information, see the topic Importing and Exporting
Models as PMML in Chapter 10 on p. 196.

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Figure 12-4
User Options dialog box, PMML tab

Export PMML. Here you can configure variations of PMML that work best with your target
application.

Select With extensions to allow PMML extensions for special cases where there is no standard
PMML equivalent. Note that in most cases this will produce the same result as standard
PMML.

Select As standard PMML... to export PMML that adheres as closely as possible to the PMML
standard.

Standard PMML Options. When the As standard PMML... option is selected, you can choose one of
two valid ways to export linear and logistic regression models:

As PMML models

For more information on PMML, see the Data Mining Group website at www.dmg.org.

Setting User Information
User/Author Information. Information you enter here can be displayed on the Annotations tab of
nodes and other objects that you create.

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Customizing the Nodes Palette
Streams are built using nodes. The Nodes Palette at the bottom of the IBM® SPSS® Modeler
window contains all of the nodes it is possible to use in stream building. For more information,
see the topic Nodes Palette in Chapter 3 on p. 18.
You can reorganize the Nodes Palette in two ways:

Customize the Palette Manager. For more information, see the topic Customizing the Palette
Manager on p. 223.

Change how palette tabs that contain subpalettes are displayed on the Nodes Palette. For more
information, see the topic Creating a Subpalette on p. 227.

Figure 12-5
Record Ops tab on the Nodes Palette

Customizing the Palette Manager
The Palette Manager can be customized to accommodate your usage of IBM® SPSS® Modeler.
For example, if you frequently analyze time-series data from a database, you might want to be
sure that the Database source node, the Time intervals node, the Time Series node, and the Time
Plot graph node are available together from a unique palette tab. The Palette Manager enables you
to easily make these adjustments by creating your custom palette tabs in the Nodes Palette.
The Palette Manager enables you to carry out various tasks:

Control which palette tabs are shown on the Nodes Palette below the stream canvas.

Change the order in which palette tabs are shown on the Nodes Palette.

Create and edit your own palette tabs and any associated subpalettes.

Edit the default node selections on your Favorites tab.

To access the Palette Manager:
E On the Tools menu, click Manage Palettes.

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Figure 12-6
Palette Manager showing the tabs displayed on the Nodes Palette

Palette Name. Each available palette tab, whether shown on the Nodes Palette or not, is listed.
This includes any palette tabs that you have created. For more information, see the topic Creating
a Palette Tab on p. 225.
No. of nodes. The number of nodes displayed on each palette tab. A high number here means you
may find it more convenient to create subpalettes to divide up the nodes on the tab. For more
information, see the topic Creating a Subpalette on p. 227.
Shown?. Select this field to display the palette tab on the Nodes Palette. For more information, see
the topic Displaying Palette Tabs on the Nodes Palette on p. 225.
Sub Palettes. To select subpalettes for display on a palette tab, highlight the required Palette
Name and click this button to display the Sub Palettes dialog box. For more information, see the
topic Creating a Subpalette on p. 227.
Restore Defaults. To completely remove all changes and additions you have made to the palettes
and subpalettes and return to the default palette settings, click this button.

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Creating a Palette Tab
Figure 12-7
Palette tab creation on the Create/Edit Palette dialog box

To create a custom palette tab:
E From the Tools menu, open the Palette Manager.
E To the right of the Shown? column, click the Add Palette button; the Create/Edit Palette dialog

box is displayed.
E Type in a unique Palette name.
E In the Nodes available area, select the node to be added to the palette tab.
E Click the Add Node right-arrow button to move the highlighted node to the Selected nodes area.

Repeat until you have added all the nodes you want.
After you have added all of the required nodes, you can change the order in which they are
displayed on the palette tab:
E Use the simple arrow buttons to move a node up or down one row.
E Use the line-arrow buttons to move a node to the bottom or top of the list.
E To remove a node from a palette, highlight the node and click the Delete button to the right
of the Selected nodes area.

Displaying Palette Tabs on the Nodes Palette
There may be options available within IBM® SPSS® Modeler that you never use; in this case,
you can use the Palette Manager to hide the tabs containing these nodes.

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Figure 12-8
Palette Manager showing the tabs displayed on the Nodes Palette

To select which tabs are to be shown on the Nodes Palette:
E From the Tools menu, open the Palette Manager.
E Using the check boxes in the Shown? column, select whether to include or hide each palette tab.

To permanently remove a palette tab from the Nodes Palette, highlight the node and click the
Delete button to the right of the Shown? column. Once deleted, a palette tab cannot be recovered.
Note: You cannot delete the default palette tabs supplied with SPSS Modeler, except for the
Favorites tab.
Changing the display order on the Nodes Palette

After you have selected which palette tabs you want to display, you can change the order in
which they are displayed on the Nodes Palette:
E Use the simple arrow buttons to move a palette tab up or down one row. Moving them up moves

them to the left of the Nodes Palette, and vice versa.
E Use the line-arrow buttons to move a palette tab to the bottom or top of the list. Those at the top of

the list will be shown on the left of the Nodes Palette.

Displaying Subpalettes on a Palette Tab
In the same way that you can control which palette tabs are displayed on the Nodes Palette, you
can control which subpalettes are available from their parent palette tab.

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Figure 12-9
Subpalettes available for the Modeling Palette tab

To select subpalettes for display on a palette tab:
E From the Tools menu, open the Palette Manager.
E Select the palette that you require.
E Click the Sub Palettes button; the Sub Palettes dialog box is displayed.
E Using the check boxes in the Shown? column, select whether to include each subpalette on the
palette tab. The All subpalette is always shown and cannot be deleted.
E To permanently remove a subpalette from the palette tab, highlight the subpalette and click the

Delete button to the right of the Shown? column.
Note: You cannot delete the default subpalettes supplied with the Modeling palette tab.
Changing the display order on the Palette Tab

After you have selected which subpalettes you want to display, you can change the order in which
they are displayed on the parent palette tab:
E Use the simple arrow buttons to move a subpalette up or down one row.
E Use the line-arrow buttons to move a subpalette to the bottom or top of the list.

The subpalettes you create are displayed on the Nodes Palette when you select their parent palette
tab. For more information, see the topic Changing a Palette Tab View on p. 228.

Creating a Subpalette
Because you can add any existing node to the custom palette tabs that you create, it is possible
that you will select more nodes than can be easily displayed on screen without scrolling. To
prevent having to scroll, you can create subpalettes into which you place the nodes you chose for

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the palette tab. For example, if you created a palette tab that contains the nodes you use most
frequently for creating your streams, you could create four subpalettes that break the selections
down by source node, field operations, modeling, and output.
Note: You can only select subpalette nodes from those added to the parent palette tab.
Figure 12-10
Subpalette creation on the Create/Edit Sub Palette dialog box

To create a subpalette:
E From the Tools menu, open the Palette Manager.
E Select the palette to which you want to add subpalettes.
E Click the Sub Palettes button; the Sub Palettes dialog box is displayed.
E To the right of the Shown? column, click the Add Sub Palette button; the Create/Edit Sub Palette

dialog box is displayed.
E Type in a unique Sub palette name.
E In the Nodes available area, select the node to be added to the subpalette.
E Click the Add Node right-arrow button to move a selected node to the Selected nodes area.
E When you have added the required nodes, click OK to return to the Sub Palettes dialog box.

The subpalettes you create are displayed on the Nodes Palette when you select their parent palette
tab. For more information, see the topic Changing a Palette Tab View on p. 228.

Changing a Palette Tab View
Due to the large number of nodes available in IBM® SPSS® Modeler, they may not all be
visible on smaller screens without scrolling to the left or right of the Nodes Palette; this is
especially noticeable on the Modeling palette tab. To reduce the need to scroll, you can choose
to display only the nodes contained in a subpalette (where available). For more information,
see the topic Creating a Subpalette on p. 227.

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To change the nodes shown on a palette tab, select the palette tab and then, from the menu on the
left, select to display either all nodes, or just those in a specific subpalette.
Figure 12-11
Modeling palette tab showing the Classification subpalette

CEMI Node Management
CEMI is now deprecated and has been replaced by CLEF, which offers a much more
flexible and easy-to-use feature set. For more information, see the IBM® SPSS®
Modeler 15 CLEF Developer’s Guide supplied with this release.

Chapter

Performance Considerations for
Streams and Nodes

You can design your streams to maximize performance by arranging the nodes in the most
efficient configuration, by enabling node caches when appropriate, and by paying attention to
other considerations as detailed in this section.
Aside from the considerations discussed here, additional and more substantial performance
improvements can typically be gained by making effective use of your database, particularly
through SQL optimization.

Order of Nodes
Even when you are not using SQL optimization, the order of nodes in a stream can affect
performance. The general goal is to minimize downstream processing; therefore, when you
have nodes that reduce the amount of data, place them near the beginning of the stream. IBM®
SPSS® Modeler Server can apply some reordering rules automatically during compilation to bring
forward certain nodes when it can be proven safe to do so. (This feature is enabled by default.
Check with your system administrator to make sure it is enabled in your installation.)
When using SQL optimization, you want to maximize its availability and efficiency. Since
optimization halts when the stream contains an operation that cannot be performed in the database,
it is best to group SQL-optimized operations together at the beginning of the stream. This strategy
keeps more of the processing in the database, so less data is carried into IBM® SPSS® Modeler.
The following operations can be done in most databases. Try to group them at the beginning of
the stream:

Merge by key (join)

Select

Aggregate

Sort

Sample

Append

Distinct operations in include mode, in which all fields are selected

Filler operations

Basic derive operations using standard arithmetic or string manipulation (depending on which
operations are supported by the database)

Set-to-flag

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The following operations cannot be performed in most databases. They should be placed in the
stream after the operations in the preceding list:

Operations on any nondatabase data, such as flat files

Merge by order

Balance

Distinct operations in discard mode or where only a subset of fields are selected as distinct

Any operation that requires accessing data from records other than the one being processed

State and count field derivations

History node operations

Operations involving “@” (time-series) functions

Type-checking modes Warn and Abort

Model construction, application, and analysis
Note: Decision trees, rulesets, linear regression, and factor-generated models can generate
SQL and can therefore be pushed back to the database.

Data output to anywhere other than the same database that is processing the data

Node Caches
To optimize stream running, you can set up a cache on any nonterminal node. When you set up a
cache on a node, the cache is filled with the data that passes through the node the next time you
run the data stream. From then on, the data is read from the cache (which is stored on disk in a
temporary directory) rather than from the data source.
Caching is most useful following a time-consuming operation such as a sort, merge, or
aggregation. For example, suppose that you have a source node set to read sales data from a
database and an Aggregate node that summarizes sales by location. You can set up a cache on the
Aggregate node rather than on the source node because you want the cache to store the aggregated
data rather than the entire data set.
Note: Caching at source nodes, which simply stores a copy of the original data as it is read into
IBM® SPSS® Modeler, will not improve performance in most circumstances.
Nodes with caching enabled are displayed with a small document icon at the top right corner.
When the data is cached at the node, the document icon is green.

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Figure 13-1
Caching at the Type node to store newly derived fields

Caching Nodes in a Database

For streams run in a database, data can be cached midstream to a temporary table in the database
rather than the file system. When combined with SQL optimization, this may result in significant
gains in performance. For example, the output from a stream that merges multiple tables to create
a data mining view may be cached and reused as needed. By automatically generating SQL for
all downstream nodes, performance can be further improved.
When using database caching with strings longer than 255 characters, either ensure that there
is a Type node upstream from the caching node and that the field values are read, or set the
string length by means of the default_sql_string_length parameter in the options.cfg file. Doing
so ensures that the corresponding column in the temporary table is set to the correct width to
accommodate the strings.
To take advantage of database caching, both SQL optimization and database caching must
be enabled. Note that Server optimization settings override those on the Client. For more
information, see the topic Setting optimization options for streams in Chapter 5 on p. 60.
With database caching enabled, simply right-click any nonterminal node to cache data at
that point, and the cache will be created automatically directly in the database the next time the
stream is run. If database caching or SQL optimization is not enabled, the cache will be written
to the file system instead.

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Performance: Process Nodes
Sort. The Sort node must read the entire input data set before it can be sorted. The data is stored in
memory up to some limit, and the excess is spilled to disk. The sorting algorithm is a combination
algorithm: data is read into memory up to the limit and sorted using a fast hybrid quick-sort
algorithm. If all the data fits in memory, then the sort is complete. Otherwise, a merge-sort
algorithm is applied. The sorted data is written to file and the next chunk of data is read into
memory, sorted, and written to disk. This is repeated until all the data has been read; then the sorted
chunks are merged. Merging may require repeated passes over the data stored on disk. At peak
usage, the Sort node will have two complete copies of the data set on disk: sorted and unsorted.
The overall running time of the algorithm is on the order of N*log(N), where N is the number
of records. Sorting in memory is faster than merging from disk, so the actual running time can
be reduced by allocating more memory to the sort. The algorithm allocates to itself a fraction of
physical RAM controlled by the IBM® SPSS® Modeler Server configuration option Memory
usage multiplier. To increase the memory used for sorting, provide more physical RAM or increase
this value. Note that when the proportion of memory used exceeds the working set of the process
so that part of the memory is paged to disk, performance degrades because the memory-access
pattern of the in-memory sort algorithm is random and can cause excessive paging. The sort
algorithm is used by several nodes other than the Sort node, but the same performance rules apply.
Binning. The Binning node reads the entire input data set to compute the bin boundaries, before
it allocates records to bins. The data set is cached while the boundaries are computed; then it is
rescanned for allocation. When the binning method is fixed-width or mean+standard deviation,
the data set is cached directly to disk. These methods have a linear running time and require
enough disk space to store the entire data set. When the binning method is ranks or tiles, the data
set is sorted using the sort algorithm described earlier, and the sorted data set is used as the cache.
Sorting gives these methods a running time of M*N*log(N), where M is the number of binned
fields and N is the number of records; it requires disk space equal to twice the data set size.
Generating a Derive node based on generated bins will improve performance in subsequent
passes. Derive operations are much faster than binning.
Merge by Key (Join). The Merge node, when the merge method is keys (equivalent to a database
join), sorts each of its input data sets by the key fields. This part of the procedure has a running
time of M*N*log(N), where M is the number of inputs and N is the number of records in the largest
input; it requires sufficient disk space to store all of its input data sets plus a second copy of the
largest data set. The running time of the merge itself is proportional to the size of the output
data set, which depends on the frequency of matching keys. In the worst case, where the output
is the Cartesian product of the inputs, the running time may approach NM. This is rare—most
joins have many fewer matching keys. If one data set is relatively larger than the other(s), or if
the incoming data is already sorted by a key field, then you can improve the performance of
this node using the Optimization tab.

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Aggregate. When the Keys are contiguous option is not set, this node reads (but does not store) its
entire input data set before it produces any aggregated output. In the more extreme situations,
where the size of the aggregated data reaches a limit (determined by the SPSS Modeler Server
configuration option Memory usage multiplier), the remainder of the data set is sorted and
processed as if the Keys are contiguous option were set. When this option is set, no data is stored
because the aggregated output records are produced as the input data is read.
Distinct. The Distinct node stores all of the unique key fields in the input data set; in cases where
all fields are key fields and all records are unique it stores the entire data set. By default the
Distinct node sorts the data on the key fields and then selects (or discards) the first distinct record
from each group. For smaller data sets with a low number of distinct keys, or those that have been
pre-sorted, you can choose options to improve the speed and efficiency of processing.
Type. In some instances, the Type node caches the input data when reading values; the cache is
used for downstream processing. The cache requires sufficient disk space to store the entire
data set but speeds up processing.
Evaluation. The Evaluation node must sort the input data to compute tiles. The sort is repeated for
each model evaluated because the scores and consequent record order are different in each case.
The running time is M*N*log(N), where M is the number of models and N is the number of records.

Performance: Modeling Nodes
Neural Net and Kohonen. Neural network training algorithms (including the Kohonen algorithm)
make many passes over the training data. The data is stored in memory up to a limit, and the
excess is spilled to disk. Accessing the training data from disk is expensive because the access
method is random, which can lead to excessive disk activity. You can disable the use of disk
storage for these algorithms, forcing all data to be stored in memory, by selecting the Optimize
for speed option on the Model tab of the node’s dialog box. Note that if the amount of memory
required to store the data is greater than the working set of the server process, part of it will be
paged to disk and performance will suffer accordingly.
When Optimize for memory is enabled, a percentage of physical RAM is allocated to the
algorithm according to the value of the IBM® SPSS® Modeler Server configuration option
Modeling memory limit percentage. To use more memory for training neural networks, either
provide more RAM or increase the value of this option, but note that setting the value too high
will cause paging.
The running time of the neural network algorithms depends on the required level of accuracy.
You can control the running time by setting a stopping condition in the node’s dialog box.
K-Means. The K-Means clustering algorithm has the same options for controlling memory usage
as the neural network algorithms. Performance on data stored on disk is better, however, because
access to the data is sequential.

Performance: CLEM Expressions
CLEM sequence functions (“@ functions”) that look back into the data stream must store
enough of the data to satisfy the longest look-back. For operations whose degree of look-back
is unbounded, all values of the field must be stored. An unbounded operation is one where the

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offset value is not a literal integer; for example, @OFFSET(Sales, Month). The offset value is the
field name Month, whose value is unknown until executed. The server must save all values of the
Sales field to ensure accurate results. Where an upper bound is known, you should provide it as
an additional argument; for example, @OFFSET(Sales, Month, 12). This operation instructs the
server to store no more than the 12 most recent values of Sales. Sequence functions, bounded or
otherwise, almost always inhibit SQL generation.

Appendix

Accessibility in IBM SPSS Modeler

Overview of Accessibility in IBM SPSS Modeler
This release offers greatly enhanced accessibility for all users, as well as specific support for users
with visual and other functional impairments. This section describes the features and methods of
working using accessibility enhancements, such as screen readers and keyboard shortcuts.

Types of Accessibility Support
Whether you have a visual impairment or are dependent on the keyboard for manipulation, there is
a wide variety of alternative methods for using this data mining toolkit. For example, you can
build streams, specify options, and read output, all without using the mouse. Available keyboard
shortcuts are listed in the topics that follow. Additionally, IBM® SPSS® Modeler provides
extensive support for screen readers, such as JAWS for Windows. You can also optimize the color
scheme to provide additional contrast. These types of support are discussed in the following topics.

Accessibility for the Visually Impaired
There are a number of properties you can specify in IBM® SPSS® Modeler that will enhance
your ability to use the software.
Display Options

You can select colors for the display of graphs. You can also choose to use your specific Windows
settings for the software itself. This may help to increase visual contrast.
E To set display options, on the Tools menu, click User Options.
E Click the Display tab. The options on this tab include the software color scheme, chart colors, and

font sizes for nodes.
Use of Sounds for Notification

By turning on or off sounds, you can control the way you are alerted to particular operations in the
software. For example, you can activate sounds for events such as node creation and deletion
or the generation of new output or models.
E To set notification options, on the Tools menu, click User Options.
E Click the Notifications tab.
© Copyright IBM Corporation 1994, 2012.

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Controlling the Automatic Launching of New Windows

The Notifications tab on the User Options dialog box is also used to control whether newly
generated output, such as tables and charts, are launched in a separate window. It may be easier
for you to disable this option and open an output window only when required.
E To set these options, on the Tools menu, click User Options.
E Click the Notifications tab.
E In the dialog box, select New Output from the list in the Visual Notifications group.
E Under Open Window, select Never.

Node Size

Nodes can be displayed using either a standard or small size. You may want to adjust these
sizes to fit your needs.
E To set node size options, on the File menu, click Stream Properties.
E Click the Layout tab.
E From the Icon Size list, select Standard.

Accessibility for Blind Users
Support for blind users is predominately dependent on the use of a screen reader, such as JAWS
for Windows. To optimize the use of a screen reader with IBM® SPSS® Modeler, you can
specify a number of settings.
Display Options

Screen readers tend to perform better when the visual contrast is greater on the screen. If you
already have a high-contrast Windows setting, you can choose to use these Windows settings
for the software itself.
E To set display options, on the Tools menu, click User Options.
E Click the Display tab.

Use of Sounds for Notification

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Controlling the Automatic Launching of New Windows

The Notifications tab on the User Options dialog box is also used to control whether newly
generated output is launched in a separate window. It may be easier for you to disable this option
and open an output window as needed.
E To set these options, on the Tools menu, click User Options.
E Click the Notifications tab.
E In the dialog box, select New Output from the list in the Visual Notifications group.
E Under Open Window, select Never.

Keyboard Accessibility
The product’s functionality is accessible from the keyboard. At the most basic level, you can press
Alt plus the appropriate key to activate window menus (such as Alt+F to access the File menu) or
press the Tab key to scroll through dialog box controls. However, there are special issues related
to each of the product’s main windows and helpful hints for navigating dialog boxes.
This section will cover the highlights of keyboard accessibility, from opening a stream to using
node dialog boxes to working with output. Additionally, lists of keyboard shortcuts are provided
for even more efficient navigation.

Shortcuts for Navigating the Main Window
You do most of your data mining work in the main window of IBM® SPSS® Modeler. The main
area is called the stream canvas and is used to build and run data streams. The bottom part of the
window contains the node palettes, which contain all available nodes. The palettes are organized
on tabs corresponding to the type of data mining operation for each group of nodes. For example,
nodes used to bring data into SPSS Modeler are grouped on the Sources tab, and nodes used to
derive, filter, or type fields are grouped on the Field Ops tab (short for Field Operations).
The right side of the window contains several tools for managing streams, output, and projects.
The top half on the right contains the managers and has three tabs that are used to manage
streams, output, and generated models. You can access these objects by selecting the tab and an
object from the list. The bottom half on the right contains the project pane, which allows you to
organize your work into projects. There are two tabs in this area reflecting two different views of a
project. The Classes view sorts project objects by type, while the CRISP-DM view sorts objects
by the relevant data mining phase, such as Data Preparation or Modeling. These various aspects of
the SPSS Modeler window are discussed throughout the Help system and User’s Guide.
Following is a table of shortcuts used to move within the main SPSS Modeler window and
build streams. Shortcuts for dialog boxes and output are listed in the topics that follow. Note that
these shortcut keys are available only from the main window.
Main Window Shortcuts
Shortcut Key
Ctrl+F5

Function
Moves focus to the node palettes.

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Shortcut Key
Ctrl+F6
Ctrl+F7
Ctrl+F8

Function
Moves focus to the stream canvas.
Moves focus to the managers pane.
Moves focus to the project pane.

Node and Stream Shortcuts
Shortcut Key
Ctrl+N
Ctrl+O
Ctrl+number keys

Ctrl+Down Arrow
Ctrl+Up Arrow
Enter

Ctrl+Enter

Alt+Enter

Shift+Spacebar

Ctrl+Shift+Spacebar

Left/Right Arrow

Up/Down Arrow

Alt+Left/Right Arrow
Alt+Up/Down Arrow
Ctrl+A
Ctrl+Q
Ctrl+W
Ctrl+Alt+D

Function
Creates a new blank stream canvas.
Displays the Open dialog box, from where you can select and open
an existing stream.
Moves focus to the corresponding tab on a window or pane. For
example, within a tabbed pane or window, Ctrl+1 moves to the first
tab starting from the left, Ctrl+2 to the second, etc.
Used in the node palette to move focus from a palette tab to the
first node under that tab.
Used in the node palette to move focus from a node to its palette tab.
When a node is selected in the node palette (including refined
models in the generated models palette), this keystroke adds the
node to the stream canvas. Pressing Enter when a node is already
selected on the canvas opens the dialog box for that node.
When a node is selected in the palette, adds that node to the stream
canvas without selecting it, and moves focus to the first node in
the palette.
When a node is selected in the palette, adds that node to the stream
canvas and selects it, while moving focus to the first node in the
palette.
When a node or comment has focus in the palette, toggles between
selecting and deselecting that node or comment.
If any other nodes or comments are also selected, this causes them
to be deselected.
When a node or comment has focus in the stream, or a node or
comment has focus on the palette, toggles between selecting and
deselecting the node or comment.
This does not affect any other selected nodes or comments.
If the stream canvas has focus, moves the entire stream horizontally
on the screen. If a palette tab has focus, cycles between tabs. If a
palette node has focus, moves between nodes in the palette.
If the stream canvas has focus, moves the entire stream vertically on
the screen. If a palette node has focus, moves between nodes in the
palette. If a subpalette has focus, moves between other subpalettes
for this palette tab.
Moves selected nodes and comments on the stream canvas
horizontally in the direction of the arrow key.
Moves selected nodes and comments on the stream canvas vertically
in the direction of the arrow key.
Selects all nodes in a stream.
When a node has focus, selects it and all nodes downstream, and
deselects all nodes upstream.
When a selected node has focus, deselects it and all selected nodes
downstream.
Duplicates a selected node.

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Shortcut Key
Ctrl+Alt+L

Ctrl+Alt+R
Ctrl+Alt+U
Ctrl+Alt+C
Ctrl+Alt+F
Tab

Shift+Tab
Ctrl+Tab

Any alphabetic key
F1
F2

F3
F6
F10
Shift+F10
Delete
Esc
Ctrl+Alt+X
Ctrl+Alt+Z
Ctrl+Alt+Shift+Z
Ctrl+E

Function
When a model nugget is selected in the stream, opens an Insert
dialog box to enable you to load a saved model from a .nod file
into the stream.
Displays the Annotations tab for a selected node, enabling you to
rename the node.
Creates a User Input source node.
Toggles the cache for a node on or off.
Flushes the cache for a node.
On the stream canvas, cycles through all the source nodes and
comments in the current stream. On a node palette, moves between
nodes in the palette. On a selected subpalette, moves to the first
node in the subpalette.
Performs the same operation as Tab but in reverse order.
With focus on the managers pane or project pane, moves focus
to the stream canvas. With focus on a node palette, moves focus
between a node and its palette tab.
With focus on a node in the current stream, gives focus and cycles
to the next node whose name starts with the key pressed.
Opens the Help system at a topic relevant to the focus.
Starts the connection process for a node selected in the canvas. Use
the Tab key to move to the required node on the canvas, and press
Shift+Spacebar to finish the connection.
Deletes all connections for the selected node on the canvas.
Moves focus between the managers pane, project pane and node
palettes.
Opens the File menu.
Opens the pop-up menu for the node or stream.
Deletes a selected node from the canvas.
Closes a pop-up menu or dialog box.
Expands a SuperNode.
Zooms in on a SuperNode.
Zooms out of a SuperNode.
With focus in the stream canvas, this runs the current stream.

A number of standard shortcut keys are also used in SPSS Modeler, such as Ctrl+C to copy. For
more information, see the topic Using Shortcut Keys in Chapter 3 on p. 26.

Shortcuts for Dialog Boxes and Tables
Several shortcut and screen reader keys are helpful when you are working with dialog boxes,
tables, and tables in dialog boxes. A complete list of special keyboard and screen reader shortcuts
follows.
Dialog Box and Expression Builder Shortcuts
Shortcut Key
Alt+4

Function
Used to dismiss all open dialog boxes or output windows. Output
can be retrieved from the Outputs tab in the managers pane.

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Shortcut Key
Ctrl+End
Ctrl+1
Ctrl+2
Ctrl+3

Function
With focus on any control in the Expression Builder, this will
move the insertion point to the end of the expression.
In the Expression Builder, moves focus to the expression edit
control.
In the Expression Builder, moves focus to the function list.
In the Expression Builder, moves focus to the field list.

Table Shortcuts

Table shortcuts are used for output tables as well as table controls in dialog boxes for nodes such
as Type, Filter, and Merge. Typically, you will use the Tab key to move between table cells and
Ctrl+Tab to leave the table control. Note: Occasionally, a screen reader may not immediately
begin reading the contents of a cell. Pressing the arrow keys once or twice will reset the software
and start the speech.
Shortcut Key
Ctrl+W
Ctrl+Alt+W

Ctrl+D
Ctrl+Alt+D

Ctrl+T
Ctrl+Alt+T
Ctrl+R
Ctrl+Alt+R
Ctrl+I
Ctrl+Alt+I
Ctrl+G
Ctrl+Alt+G
Ctrl+Q

Function
For tables, reads the short description of the selected roW. For
example, “Selected row 2 values are sex, flag, m/f, etc.”
For tables, reads the long description of the selected roW. For
example, “Selected row 2 values are field = sex, type = flag,
sex = m/f, etc.”
For tables, reads the short Description of the selected area. For
example, “Selection is one row by six columns.”
For tables, provides the long Description of the selected area.
For example, “Selection is one row by six columns. Selected
columns are Field, Type, Missing. Selected row is 1.”
For tables, provides a short description of the selected columns.
For example, “Fields, Type, Missing.”
For tables, provides a long description of the selected columns.
For example, “Selected columns are Fields, Type, Missing.”
For tables, provides the number of Records in the table.
For tables, provides the number of Records in the table as well
as column names.
For tables, reads the cell Information, or contents, for the cell
that has focus.
For tables, reads the long description of cell Information
(column name and contents of the cell) for the cell that has focus.
For tables, provides short General selection information.
For tables, provides long General selection information.
For tables, provides a Quick toggle of the table cells. Ctrl+Q
reads long descriptions, such as “Sex=Female,” as you move
through the table using the arrow keys. Selecting Ctrl+Q again
will toggle to short descriptions (cell contents).

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Appendix A

Shortcuts for Comments
When working with on-screen comments, you can use the following shortcuts.
Shortcut Key
Alt+C
Alt+M
Tab
Enter
Alt+Enter or Ctrl+Tab
Esc
Alt+Shift+Up Arrow
Alt+Shift+Down Arrow
Alt+Shift+Left Arrow
Alt+Shift+Right Arrow

Function
Toggles the show/hide comment feature.
Inserts a new comment if comments are currently displayed;
shows comments if they are currently hidden.
On the stream canvas, cycles through all the source nodes and
comments in the current stream.
When a comment has focus, indicates the start of editing.
Ends editing and saves editing changes.
Cancels editing. Changes made during editing are lost.
Reduces the height of the text area by one grid cell (or one pixel)
if snap-to-grid is on (or off).
Increases the height of the text area by one grid cell (or one
pixel) if snap-to-grid is on (or off).
Reduces the width of the text area by one grid cell (or one pixel)
if snap-to-grid is on (or off).
Increases the width of the text area by one grid cell (or one pixel)
if snap-to-grid is on (or off).

Shortcuts for Cluster Viewer and Model Viewer
Shortcut keys are available for navigating around the Cluster Viewer and Model Viewer windows.
General - Cluster Viewer and Model Viewer
Shortcut Key
Tab
Shift+Tab
Down Arrow

Up Arrow

Enter
F6
Left and Right Arrows
Alt+letter
Esc

Function
Moves focus to the next screen control.
Moves focus to the previous screen control.
If a drop-down list has focus, opens the list or moves to the next
item on the list.
If a menu has focus, moves to the next item on the menu.
If a thumbnail graph has focus, moves to the next one in the set
(or to the first one if the last thumbnail has focus).
If a drop-down list is open, moves to the previous item on the list.
If a menu has focus, moves to the previous item on the menu.
If a thumbnail graph has focus, moves to the previous one in the
set (or to the last one if the first thumbnail has focus).
Closes an open drop-down list, or makes a selection on an open
menu.
Toggles focus between the left- and right-hand panes of the
window.
If a tab has focus, moves to the previous or next tab.
If a menu has focus, moves to the previous or next menu.
Selects the button or menu having this letter underlined in its
name.
Closes an open menu or drop-down list.

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Cluster Viewer only

The Cluster Viewer has a Clusters view that contains a cluster-by-features grid.
To choose the Clusters view instead of the Model Summary view:
E Press Tab repeatedly until the View button is selected.
E Press Down Arrow twice to select Clusters.

From here you can select an individual cell within the grid:
E Press Tab repeatedly until you arrive at the last icon in the visualization toolbar.
Figure A-1
Show Visualization Tree icon

E Press Tab once more, then Spacebar, then an arrow key.

The following keyboard shortcuts are now available:
Shortcut Key
Arrow key

Ctrl+, (comma)

Tab
Shift+Tab
F2
Enter
Esc

Function
Moves focus between individual cells in the grid. The cell
distribution display in the right-hand pane changes as the focus
moves.
Selects or deselects the entire column in the grid in which a cell
has focus.
To add a column to the selection, use the arrow keys to navigate
to a cell in that column and press Ctrl+, again.
Moves focus out of the grid and onto the next screen control.
Moves focus out of the grid and back to the previous screen
control.
Enters edit mode (label and description cells only).
Saves editing changes and exits edit mode (label and description
cells only).
Exits edit mode without saving changes (label and description
cells only).

Shortcut Keys Example: Building Streams
To make the stream-building process more clear for users dependent on the keyboard or on a
screen reader, following is an example of building a stream without the use of the mouse. In this
example, you will build a stream containing a Variable File node, a Derive node, and a Histogram
node using the following steps:
E Start SPSS Modeler. When IBM® SPSS® Modeler first starts, focus is on the Favorites tab

of the node palette.
E Ctrl+Down Arrow. Moves focus from the tab itself to the body of the tab.
E Right Arrow. Moves focus to the Variable File node.

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Appendix A
E Spacebar. Selects the Variable File node.
E Ctrl+Enter. Adds the Variable File node to the stream canvas. This key combination also keeps

selection on the Variable File node so that the next node added will be connected to it.
E Tab. Moves focus back to the node palette.
E Right Arrow 4 times. Moves to the Derive node.
E Spacebar. Selects the Derive node.
E Alt+Enter. Adds the Derive node to the canvas and moves selection to the Derive node. This node

is now ready to be connected to the next added node.
E Tab. Moves focus back to the node palette.
E Right Arrow 5 times. Moves focus to the Histogram node in the palette.
E Spacebar. Selects the Histogram node.
E Enter. Adds the node to the stream and moves focus to the stream canvas.

Continue with the next example, or save the stream if you want to try the next example at a
later time.

Shortcut Keys Example: Editing Nodes
In this example, you will use the stream built in the earlier example. The stream consists of a
Variable File node, a Derive node, and a Histogram node. The instructions begin with focus on the
third node in the stream, the Histogram node.
E Ctrl+Left Arrow 2 times. Moves focus back to the Variable File node.
E Enter. Opens the Variable File dialog box. Tab through to the File field and type a text file path

and name to select that file. Press Ctrl+Tab to navigate to the lower part of the dialog box, tab
through to the OK button and press Enter to close the dialog box.
E Ctrl+Right Arrow. Gives focus to the second node, a Derive node.
E Enter. Opens the Derive node dialog box. Tab through to select fields and specify derive

conditions. Press Ctrl+Tab to navigate to the OK button and press Enter to close the dialog box.
E Ctrl+Right Arrow. Gives focus to the third node, a Histogram node.
E Enter. Opens the Histogram node dialog box. Tab through to select fields and specify graph options.

For drop-down lists, press Down Arrow to open the list and to highlight a list item, then press
Enter to select the list item. Tab through to the OK button and press Enter to close the dialog box.
At this point, you can add additional nodes or run the current stream. Keep in mind the following
tips when you are building streams:

When manually connecting nodes, use F2 to create the start point of a connection, tab to move
to the end point, then use Shift+Spacebar to finalize the connection.

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Accessibility in IBM SPSS Modeler

Use F3 to destroy all connections for a selected node in the canvas.

Once you have created a stream, use Ctrl+E to run the current stream.

A complete list of shortcut keys is available. For more information, see the topic Shortcuts for
Navigating the Main Window on p. 238.

Using a Screen Reader
A number of screen readers are available on the market. IBM® SPSS® Modeler is configured to
support JAWS for Windows using the Java Access Bridge, which is installed along with SPSS
Modeler. If you have JAWS installed, simply launch JAWS before launching SPSS Modeler
to use this product.
Due to the nature of SPSS Modeler’s unique graphical representation of the data mining
process, charts and graphs are optimally used visually. It is possible, however, for you to
understand and make decisions based on output and models viewed textually using a screen reader.
Note: With 64-bit client machines, some assistive technology features do not work. This is
because the Java Access Bridge is not designed for 64-bit operation.
Using the IBM SPSS Modeler Dictionary File

An SPSS Modeler dictionary file (Awt.JDF) is available for inclusion with JAWS. To use this file:
E Navigate to the /accessibility subdirectory of your SPSS Modeler installation and copy the

dictionary file (Awt.JDF).
E Copy it to the directory with your JAWS scripts.

You may already have a file named Awt.JDF on your machine if you have other JAVA applications
running. In this case, you may not be able to use this dictionary file without manually editing the
dictionary file.

Using a Screen Reader with HTML Output
When viewing output displayed as HTML within IBM® SPSS® Modeler using a screen reader,
you may encounter some difficulties. A number of types of output are affected, including:

Output viewed on the Advanced tab for Regression, Logistic Regression, and Factor/PCA
nodes

Report node output

In each of these windows or dialog boxes, there is a tool on the toolbar that can be used to launch
the output into your default browser, which provides standard screen reader support. You can
then use the screen reader to convey the output information.

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Appendix A

Accessibility in the Interactive Tree Window
The standard display of a decision tree model in the Interactive Tree window may cause problems
for screen readers. To access an accessible version, on the Interactive Tree menus click:
View > Accessible Window

This displays a view similar to the standard tree map, but one which JAWS can read correctly. You
can move up, down, right, or left using the standard arrow keys. As you navigate the accessible
window, the focus in the Interactive Tree window moves accordingly. Use the Spacebar to change
the selection, or use Ctrl+Spacebar to extend the current selection.

Tips for Use
There are several tips for making the IBM® SPSS® Modeler environment more accessible to you.
The following are general hints when working in SPSS Modeler.

Exiting extended text boxes. Use Ctrl+Tab to exit extended text boxes. Note: Ctrl+Tab is also
used to exit table controls.

Using the Tab key rather than arrow keys. When selecting options for a dialog box, use the Tab
key to move between option buttons. The arrow keys will not work in this context.

Drop-down lists. In a drop-down list for dialog boxes, you can use either the Escape key or the
Spacebar to select an item and then close the list. You can also use the Escape key to close
drop-down lists that do not close when you have tabbed to another control.

Execution status. When you are running a stream on a large database, JAWS can lag behind in
reading the stream status to you. Press the Ctrl key periodically to update the status reporting.

Using the node palettes. When you first enter a tab of the node palettes, JAWS will sometimes
read “groupbox” instead of the name of the node. In this case, you can use Ctrl+Right Arrow
and then Ctrl+Left Arrow to reset the screen reader and hear the node name.

Reading menus. Occasionally, when you are first opening a menu, JAWS may not read the first
menu item. If you suspect that this may have happened, use the Down Arrow and then the
Up Arrow to hear the first item in the menu.

Cascaded menus. JAWS does not read the first level of a cascaded menu. If you hear a break in
speaking while moving through a menu, press the Right Arrow key to hear the child menu
items.

Additionally, if you have IBM® SPSS® Modeler Text Analytics installed, the following tips can
make the interactive workbench interface more accessible to you.

Entering dialog boxes. You may need to press the Tab key to put the focus on the first control
upon entering a dialog box.

Exiting extended text boxes. Use Ctrl+Tab to exit extended text boxes and move to the next
control. Note: Ctrl+Tab is also used to exit table controls.

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Typing the first letter to find element in tree list. When looking for an element in the categories
pane, extracted results pane, or library tree, you can type the first letter of the element when
the pane has the focus. This will select the next occurrence of an element beginning with
the letter you entered.

Drop-down lists. In a drop-down list for dialog boxes, you can use the Spacebar to select an
item and then close the list.

Additional tips for use are discussed at length in the following topics.

Interference with Other Software
When testing IBM® SPSS® Modeler with screen readers, such as JAWS, our development team
discovered that the use of a Systems Management Server (SMS) within your organization may
interfere with JAWS’ ability to read Java-based applications, such as SPSS Modeler. Disabling
SMS will correct this situation. Visit the Microsoft website for more information on SMS.

JAWS and Java
Different versions of JAWS provide varying levels of support for Java-based software applications.
Although IBM® SPSS® Modeler will work with all recent versions of JAWS, some versions may
have minor problems when used with Java-based systems. Visit the JAWS for Windows website
at http://www.FreedomScientific.com.

Using Graphs in IBM SPSS Modeler
Visual displays of information, such as histograms, evaluation charts, multiplots, and scatterplots,
are difficult to interpret with a screen reader. Please note, however, that web graphs and
distributions can be viewed using the textual summary available from the output window.

Appendix

Unicode Support
Unicode Support in IBM SPSS Modeler

IBM® SPSS® Modeler is fully Unicode-enabled for both IBM® SPSS® Modeler and IBM®
SPSS® Modeler Server. This makes it possible to exchange data with other applications that
support Unicode, including multi-language databases, without any loss of information that might
be caused by conversion to or from a locale-specific encoding scheme.

SPSS Modeler stores Unicode data internally and can read and write multi-language data
stored as Unicode in databases without loss.

SPSS Modeler can read and write UTF-8 encoded text files. Text file import and export
will default to the locale-encoding but support UTF-8 as an alternative. This setting can be
specified in the file import and export nodes, or the default encoding can be changed in the
stream properties dialog box. For more information, see the topic Setting general options
for streams in Chapter 5 on p. 55.

Statistics, SAS, and text data files stored in the locale-encoding will be converted to UTF-8 on
import and back again on export. When writing to any file, if there are Unicode characters
that do not exist in the locale character set, they will be substituted and a warning will be
displayed. This should occur only where the data has been imported from a data source that
supports Unicode (a database or UTF-8 text file) and that contains characters from a different
locale or from multiple locales or character sets.

IBM® SPSS® Modeler Solution Publisher images are UTF-8 encoded and are truly portable
between platforms and locales.

About Unicode

The goal of the Unicode standard is to provide a consistent way to encode multilingual text so that
it can be easily shared across borders, locales, and applications. The Unicode Standard, now at
version 4.0.1, defines a character set that is a superset of all of the character sets in common use
in the world today and assigns to each character a unique name and code point. The characters
and their code points are identical to those of the Universal Character Set (UCS) defined by
ISO-10646. For more information, see the Unicode Home Page (http://www.unicode.org).

248

Appendix

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IBM may use or distribute any of the information you supply in any way it believes appropriate
without incurring any obligation to you.
Licensees of this program who wish to have information about it for the purpose of enabling: (i) the
exchange of information between independently created programs and other programs (including
this one) and (ii) the mutual use of the information which has been exchanged, should contact:
IBM Software Group, Attention: Licensing, 233 S. Wacker Dr., Chicago, IL 60606, USA.
© Copyright IBM Corporation 1994, 2012.

249

250
Appendix C

Such information may be available, subject to appropriate terms and conditions, including in
some cases, payment of a fee.
The licensed program described in this document and all licensed material available for it are
provided by IBM under terms of the IBM Customer Agreement, IBM International Program
License Agreement or any equivalent agreement between us.
Any performance data contained herein was determined in a controlled environment. Therefore,
the results obtained in other operating environments may vary significantly. Some measurements
may have been made on development-level systems and there is no guarantee that these
measurements will be the same on generally available systems. Furthermore, some measurements
may have been estimated through extrapolation. Actual results may vary. Users of this document
should verify the applicable data for their specific environment.
Information concerning non-IBM products was obtained from the suppliers of those products,
their published announcements or other publicly available sources. IBM has not tested those
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related to non-IBM products. Questions on the capabilities of non-IBM products should be
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All statements regarding IBM’s future direction or intent are subject to change or withdrawal
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To illustrate them as completely as possible, the examples include the names of individuals,
companies, brands, and products. All of these names are fictitious and any similarity to the names
and addresses used by an actual business enterprise is entirely coincidental.
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Trademarks

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http://www.ibm.com/legal/copytrade.shtml.
Intel, Intel logo, Intel Inside, Intel Inside logo, Intel Centrino, Intel Centrino logo, Celeron, Intel
Xeon, Intel SpeedStep, Itanium, and Pentium are trademarks or registered trademarks of Intel
Corporation or its subsidiaries in the United States and other countries.
Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.
Microsoft, Windows, Windows NT, and the Windows logo are trademarks of Microsoft
Corporation in the United States, other countries, or both.
UNIX is a registered trademark of The Open Group in the United States and other countries.
Java and all Java-based trademarks and logos are trademarks of Sun Microsystems, Inc. in the
United States, other countries, or both.
Other product and service names might be trademarks of IBM or other companies.

251
Notices

Index
508 compliance, 236
abs function, 138
accessibility, 236, 247
example, 243–244
features in IBM SPSS Modeler, 236
tips in IBM SPSS Modeler, 246
adding
to a project, 202
adding IBM SPSS Modeler Server connections, 14, 16
Aggregate node
performance, 234
allbutfirst function, 141
allbutlast function, 141
alphabefore function, 141
and operator, 137
annotating
nodes, 78, 86
streams, 78, 86
annotations
converting to comments, 85
folder, 207
project, 206
application examples, 4
applications, 30
applications of data mining, 30
arccos function, 139
arccosh function, 139
arcsin function, 139
arcsinh function, 139
arctan function, 139
arctan2 function, 139
arctanh function, 139
attribute, 29
automation, 105
backslash character in CLEM expressions, 129
backup stream files
restoring, 88
Binning node
performance, 233
bitwise functions, 140
@BLANK function, 102, 134, 156
blank handling
CLEM functions, 156
blanks, 99–100, 113
branches, modeling and scoring, 78, 188–189, 194
build rule node
loading, 90
cache
enabling, 50, 216, 231
flushing, 52, 57
options for nodes, 50, 231
© Copyright IBM Corporation 1994, 2012.

saving, 52
setting up a cache, 48
cache file node
loading, 90
canvas, 18
case, 29
cdf_chisq function, 139
cdf_f function, 139
cdf_normal function, 139
cdf_t function, 139
Champion Challenger analysis, 160, 185
characters, 127–128
charts
saving output, 89
checking CLEM expressions, 123
chi-square distribution
probability functions, 139
classes , 20, 200, 202
CLEM, 117
building expressions, 119
checking expressions, 123
datatypes, 128–129
examples, 108
expressions, 111, 127
functions, 120
introduction, 27, 105
language, 127
CLEM expressions
finding and replacing text, 123
parameters, 68, 112
performance, 234
CLEM functions
bitwise, 140
blanks and nulls, 156
comparison, 135
conversion, 135
datetime, 146
global, 155
information, 134
list of available, 133
logical, 137
missing values, 102
numeric, 138
probability, 139
random, 141
sequence, 150, 152
special functions, 157
string, 141
trigonometric, 139
client
default directory, 216
colors
setting, 220
comma, 56
command line
starting IBM SPSS Modeler, 13
252

253
Index

comments
keyboard shortcuts, 242
listing all in a stream, 84
on nodes and streams, 78
comparison functions, 135
concatenating strings, 135
conditions, 111
connections
server cluster, 16
to IBM SPSS Collaboration and Deployment Services
Repository, 161–162
to IBM SPSS Modeler Server, 13–14, 16
conventions, 133
conversion functions, 135
Coordinator of Processes, 16
COP, 16
copy, 21
cos function, 139
cosh function, 139
count_equal function, 115, 135
count_greater_than function, 115, 135
count_less_than function, 115, 135
count_non_nulls function, 135
count_not_equal function, 115, 135
count_nulls function, 102, 115, 135
count_substring function, 141
CRISP-DM, 20, 200
projects view, 201
CRISP-DM process model, 32
currency display format, 59
custom palette creation, 225
subpalette creation, 227
cut, 21
data
preview, 52
data audit node
use in exploration, 29
Data Audit node
use in data mining, 31
data mapping tool, 91–92
data mining, 29
application examples, 40
strategy, 32
data streams
building, 41
data types, 110
in parameters, 70
database
functions, 120
date formats, 58, 129–130
date functions, 129–130
date_before, 135, 146
date_days_difference, 146
date_in_days, 146
date_in_months, 146
date_in_weeks, 146

date_in_years, 146
date_months_difference, 146
date_weeks_difference, 146
date_years_difference, 146
@TODAY function, 146
date_before function, 135
date/time values, 114
dates
converting, 150
manipulating, 150
datetime functions
datetime_date, 146
datetime_day, 146
datetime_day_name, 146
datetime_day_short_name, 146
datetime_hour, 146
datetime_in_seconds, 146
datetime_minute, 146
datetime_month, 146
datetime_month_name, 146
datetime_month_short_name, 146
datetime_now datetime_second, 146
datetime_time, 146
datetime_timestamp, 146
datetime_weekday , 146
datetime_year, 146
datetime_date function, 135
decimal places
display formats, 59
decimal symbol
number display formats, 56
decision trees
accessibility, 246
default
project phase, 201
degrees
measurements units, 60
deploying scenarios, 185
deployment, 160
deployment options
scenarios, 185
deployment type, 185
dictionary file, 245
DIFF function, 152
@DIFF function, 150, 152
directory
default, 216
disable nodes , 46, 48
display formats
currency, 59
decimal places, 59
grouping symbol, 59
numbers, 59
scientific, 59
Distinct node
performance, 234
distribution functions, 139

254
Index

div function, 138
documentation, 4
domain name (Windows)
IBM SPSS Modeler Server, 13
DTD, 197
enable nodes , 46
encoding, 56, 248
endstring function, 141
Enterprise View node, 185
equals operator, 135
error messages, 65
essential fields, 91, 94
Evaluation node
performance, 234
examples
Applications Guide, 4
overview, 5
execution times, viewing, 67
exponential function, 138
exporting
PMML, 196, 198
stream descriptions, 77
Expression Builder, 240
accessing, 119
finding and replacing text, 123
overview, 117
using, 119
expressions, 127
f distribution
probability functions, 139
factor, 245
Feature Selection node
missing values, 101
@FIELD function, 102, 157
fields, 29, 127, 129
in CLEM expressions, 121
viewing values, 122
@FIELDS_BETWEEN function, 102, 115, 157
@FIELDS_MATCHING function, 102, 115, 157
filler node
missing values, 102
finding text, 123
first_index function, 117, 135
first_non_null function, 117, 135
first_non_null_index function, 117, 135
folders, IBM SPSS Collaboration and Deployment
Services Repository, 177, 179
fonts, 220
fracof function, 138
functions, 129–130, 133–134, 150
@BLANK, 102
database, 120
examples, 108
@FIELD, 117, 157
@GLOBAL_MAX, 155

@GLOBAL_MEAN, 155
@GLOBAL_MIN, 155
@GLOBAL_SDEV, 155
@GLOBAL_SUM, 155
handling missing values, 102
in CLEM expressions, 120
@PARTITION, 157
@PREDICTED, 117, 157
@TARGET, 117, 157
user-defined functions (UDFs), 120
generated models palette, 19
global functions, 155
global values
in CLEM expressions, 121
graphs
adding to projects, 202
saving output, 89
greater than operator, 135
grouping symbol
number display formats, 56
hasendstring function, 141
hasmidstring function, 141
hasstartstring function, 141
hassubstring function, 141
hints
general usage, 96
host name
IBM SPSS Modeler Server, 13–14
hot keys, 26
HTML output
screen reader, 245
IBM InfoSphere Warehouse (ISW)
PMML export, 198
IBM SPSS Collaboration and Deployment Services, 160
IBM SPSS Collaboration and Deployment Services
Enterprise View, 160, 185
IBM SPSS Collaboration and Deployment Services
Repository, 158, 160
browsing, 162
connecting to, 161–162
deleting objects and versions, 178
folders, 177, 179
locking and unlocking objects, 177
object properties, 180
retrieving objects, 172
searching in, 175
single sign-on, 161
storing objects, 164
transferring projects to, 204
IBM SPSS Modeler, 1, 17
accessibility features, 236
documentation, 4
getting started, 12

255
Index

options, 215
overview, 12, 215
running from command line, 13
tips and shortcuts, 96
IBM SPSS Modeler Advantage, 160, 184
IBM SPSS Modeler Server
domain name (Windows), 13
host name, 13–14
password, 13
port number, 13–14
user ID, 13
IBM SPSS Statistics models, 39
icons
setting options, 24, 64
if, then, else functions, 137
importing
PMML, 197–198
INDEX function, 152
@INDEX function, 150, 152
information functions, 134
integer_bitcount function, 140
integer_leastbit function, 140
integer_length function, 140
integers, 127–128
Interactive Tree window
accessibility, 246
intof function, 138
introduction, 127
IBM SPSS Modeler, 12, 215
is_date function, 134
is_datetime function, 134
is_integer function, 134
is_number function, 134
is_real function, 134
is_string function, 134
is_time function, 134
is_timestamp function, 134
isalphacode function, 141
isendstring function, 141
islowercode function, 141
ismidstring function, 141
isnumbercode function, 141
isstartstring function, 141
issubstring function, 141
issubstring_count function, 141
issubstring_lim function, 141
isuppercode function, 141
Java, 247
JAWS, 236, 245, 247
K-Means node
large sets, 57
performance, 234
keyboard shortcuts, 238, 240, 242
keywords
annotating nodes, 86

knowledge discovery, 29
Kohonen node
large sets, 57
performance, 234
labels
displaying, 57
value, 197
variable, 197
labels, IBM SPSS Collaboration and Deployment Services
Repository object, 183
language
options, 215
last_index function, 117, 135
LAST_NON_BLANK function, 152
@LAST_NON_BLANK function, 150, 152, 156
last_non_null function, 117, 135
last_non_null_index function, 117, 135
legal notices, 249
length function, 141
less than operator, 135
linear regression
export as PMML, 222
listing all comments for a stream, 84
lists, 127, 129
loading
nodes, 90
states, 90
locale
options, 215
locchar function, 141
locchar_back function, 141
locking IBM SPSS Collaboration and Deployment
Services Repository objects, 177
locking nodes , 53
log files
displaying generated SQL, 63
log function, 138
log10 function, 138
logging in to IBM SPSS Modeler Server, 13
logical functions, 137
logistic regression, 245
export as PMML, 222
lowertoupper function, 141
machine learning, 29
main window, 18
managers, 19
mandatory fields, 95
mapping data, 94
mapping fields, 91
matches function, 141
max function, 135
MAX function, 152
@MAX function, 150, 152
max_index function, 117, 135
max_n function, 115, 135

256
Index

MEAN function, 150, 152
@MEAN function, 150, 152
mean_n function, 115, 138
member function, 135
memory
managing, 215–216
Merge node
performance, 233
messages
displaying generated SQL, 63
middle mouse button
simulating, 26, 43
min function, 135
MIN function, 152
@MIN function, 150, 152
min_index function, 117, 135
min_n function, 115, 135
minimizing, 23
missing values, 100–101, 113
CLEM expressions, 102
filling, 99
handling, 99
in records, 101
mod function, 138
model nuggets, 78
model refresh, 185
modeling
branch, 78
modeling nodes, 34, 42
modeling palette tab customization, 228
performance, 234
models, 78
adding to projects, 202
exporting, 221
refreshing, 190
replacing, 219
storing in the IBM SPSS Collaboration and Deployment
Services Repository, 172
models palette, 172
mouse
using in IBM SPSS Modeler, 26, 43
@MULTI_RESPONSE_SET function, 117, 157
multiple IBM SPSS Modeler sessions, 17
multiple-category sets
in CLEM expressions, 117
multiple-dichotomy sets
in CLEM expressions, 117
multiple-response sets
in CLEM expressions, 117, 121
naming nodes and streams, 86
navigating
keyboard shortcuts, 238
negate function, 138
neural net node
large sets, 57

Neural Net node
performance, 234
new features, 7, 10
node caching
enabling, 50, 231
node names, 86
node palette selection, 225
nodes, 12
adding, 43, 46
adding comments to, 78
adding to projects, 202–203
annotating, 78, 86
bypassing in a stream, 45
connecting in a stream, 43
custom palette creation, 225
custom subpalette creation, 227
data preview, 52
deleting, 43
deleting connections, 47
disabling, 46, 48
disabling in a stream, 46
displaying on palette, 225
duplicating, 48
editing, 48
enabling, 46
execution times, 67
introduction, 42
loading, 90
locking, 53
order of, 230
palette tab customization, 228
performance, 233–234
previewing data, 52
removing from palette, 225
saving, 88
searching for, 73
setting options, 48
storing in the IBM SPSS Collaboration and Deployment
Services Repository, 171
noisy data, 31
normal distribution
probability functions, 139
not equal operator, 135
not operator, 137
notifications
setting options, 217
nuggets, 78
defined, 20
@NULL function, 102, 134, 156
nulls, 99, 113
number display formats, 59
numbers, 114, 128
numeric functions, 138
object properties, IBM SPSS Collaboration and
Deployment Services Repository, 180

257
Index

objects
properties, 208
OFFSET function, 152
@OFFSET function, 150, 152
performance considerations, 234
oneof function, 141
opening
models, 90
nodes, 90
output, 90
projects, 202
states, 90
streams, 90
operator precedence, 131
operators
in CLEM expressions, 120
joining strings, 135
optimization, 60
options, 215
display, 220
for IBM SPSS Modeler, 215
PMML, 221
stream properties, 54–55, 57, 59–60, 63–65, 67
user, 217
or operator, 137
output, 19
output files
saving, 89
output nodes, 42
output objects
storing in the IBM SPSS Collaboration and Deployment
Services Repository, 171
palette tab customization, 228
palettes, 18
customizing, 223
parallel processing
enabling, 60
parameters
in CLEM expressions, 121
model building, 188
runtime prompts, 70
scoring, 188
session, 68, 70, 112
stream, 68, 70, 112
type, 70
using in scenarios, 188
@PARTITION_FIELD function, 157
password
IBM SPSS Modeler Server, 13
paste, 21
performance
CLEM expressions, 234
node caching, 50, 231
of modeling nodes, 234
of process nodes, 233
period, 56

pi function, 139
PMML
export options, 221
exporting models, 196, 198
importing models, 197–198
PMML models
linear regression, 222
logistic regression, 222
port number
IBM SPSS Modeler Server, 13–14
power (exponential) function, 138
PowerPoint files, 202
precedence, 131
@PREDICTED function, 157
Predictive Applications, 185
preview
node data, 52
printing, 27
streams, 24, 48
probability functions, 139
process nodes, 42
performance, 233
projects, 20, 200
adding objects, 203
annotating, 206
building, 202
Classes view, 202
closing, 209
creating new, 202
CRISP-DM view, 201
folder properties, 207
generating reports, 209
in the IBM SPSS Collaboration and Deployment
Services Repository, 204
object properties, 208
setting a default folder, 201
setting properties, 205
storing in the IBM SPSS Collaboration and Deployment
Services Repository, 170
prompts, runtime, 70
properties
for data streams, 54
project folder, 207
report phases, 209
purple nodes, 60
pushbacks, 60
Quality node
missing values, 101
radians
measurements units, 60
random function, 141
random0 function, 141
reals, 127–128
records, 29
missing values, 101

258
Index

refresh
source nodes, 57
refreshing models, 190
regression, 245
rem function, 138
renaming
nodes, 86
streams, 74
replace function, 141
replacing models, 219
replacing text, 123
replicate function, 141
reports
adding to projects, 202
generating, 209
saving output, 89
setting properties, 209
resizing, 23
retrieving objects from the IBM SPSS Collaboration and
Deployment Services Repository, 172
rollover days, 58
round function, 138
rule sets
evaluating, 56
running streams, 77
SAS files
encoding, 248
saving
multiple objects, 89
nodes, 88
output objects, 89
states, 88
streams, 88
scaling streams to view, 24
scenarios, 184
defined, 160
deployment options, 185
scientific notation
display format, 59
scoring
branch, 78, 188–189, 194
screen readers, 238, 240, 245–246
example, 243–244
scripting, 27, 105
finding and replacing text, 123
scrolling
setting options, 64
SDEV function, 152
@SDEV function, 150, 152
sdev_n function, 115, 138
searching
for nodes in a stream, 73
searching COP for connections, 16
searching for objects in the IBM SPSS Collaboration and
Deployment Services Repository, 175
sequence functions, 150, 152

server
adding connections, 14
default directory, 216
logging in, 13
searching COP for servers, 16
session parameters, 68, 70, 112
set command, 68, 112
sets, 57
shortcuts
general usage, 96
keyboard, 26, 238, 240, 242
sign function, 138
sin function, 139
SINCE function, 152
@SINCE function, 150, 152
single sign-on, 14, 161
single sign-on, IBM SPSS Collaboration and Deployment
Services Repository, 158, 161
sinh function, 139
skipchar function, 141
skipchar_back function, 141
Sort node
performance, 233
soundex function, 146
soundex_difference function, 146
source nodes, 42
data mapping, 92
refreshing, 57
spaces
removing from strings, 112, 141
special characters
removing from strings, 112
special functions, 157
SPSS Modeler Server, 2
SQL generation, 60
logging, 63
previewing, 63
sqrt function, 138
startstring function, 141
startup dialog box, 220
states
loading, 90
saving, 88
Statistics files
encoding, 248
Statistics models, 39
stop execution, 21
storing objects in the IBM SPSS Collaboration and
Deployment Services Repository, 164
stream, 18
stream canvas
settings, 64
stream default encoding, 56
stream descriptions, 74, 77
stream names, 86
stream parameters, 68, 70, 112

259
Index

stream rewriting
enabling, 60
streams, 12
adding comments, 78
adding nodes, 43, 46
adding to projects, 202–203
annotating, 78, 86
backup files, 88
building, 41
bypassing nodes, 45
connecting nodes, 43
deployment options, 185
disabling nodes, 46
loading, 90
options, 54–55, 57, 59–60, 63–64
renaming, 74, 86
running, 77
saving, 88
scaling to view, 24
storing in the IBM SPSS Collaboration and Deployment
Services Repository, 170
viewing execution times, 67
string functions, 141
strings, 127, 129
manipulating in CLEM expressions, 112
matching, 112
replacing, 112
stripchar function, 141
strmember function, 141
subpalette
creation, 227
displaying on palette tab, 226
removing from palette tab, 226
subscrs function, 141
substring function, 141
substring_between function, 141
SUM function, 152
@SUM function, 150, 152
sum_n function, 115, 138
SuperNode
parameters, 68, 112
system
options, 215
system-missing values, 99
t distribution
probability functions, 139
tables, 240
adding to projects, 202
saving output, 89
tan function, 139
tanh function, 139
@TARGET function, 157
temp directory, 16
template fields, 95
templates, 92
terminal nodes, 42

testbit function, 140
@TESTING_PARTITION function, 157
text data files
encoding, 248
text encoding, 56
THIS function, 152
@THIS function, 150, 152
time and date functions, 129–130
time fields
converting , 150
time formats, 58, 129–130
time functions, 129–130
time_before, 135, 146
time_hours_difference, 146
time_in_hours, 146
time_in_mins, 146
time_in_secs, 146
time_mins_difference, 146
time_secs_difference, 146
time_before function, 135
tips
for accessibility, 246
general usage, 96
to_date function, 135, 146
to_dateline function, 146
to_datetime function, 135
to_integer function, 135
to_number function, 135
to_real function, 135
to_string function, 135
to_time function, 135, 146
to_timestamp function, 135, 146
@TODAY function, 146
toolbar, 21
ToolTips
annotating nodes, 86
trademarks, 250
@TRAINING_PARTITION function, 157
tree-based analysis
typical applications, 30
trigonometric functions, 139
trim function, 141
trim_start function, 141
trimend function, 141
Type node
missing values, 102
performance, 234
typical applications, 30
undef function, 156
undo, 21
Unicode support, 248
unicode_char function, 141
unicode_value function, 141
unlocking IBM SPSS Collaboration and Deployment
Services Repository objects, 177
unmapping fields, 91

260
Index

uppertolower function, 141
user ID
IBM SPSS Modeler Server, 13
user options, 217
user-defined functions (UDFs), 120
user-missing values, 99
UTF-8 encoding, 56, 248
@VALIDATION_PARTITION function, 157
value_at function, 117, 135
values, 110
adding to CLEM expressions, 122
viewing from a data audit, 122
variables, 29
version labels, IBM SPSS Collaboration and Deployment
Services Repository object, 183
visual programming, 17
warnings, 65
setting options, 217
welcome dialog box, 220
white space
removing from strings, 112, 141
zooming, 21

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Title                           : IBM SPSS Modeler 15 User’s Guide
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