IBM SPSS Modeler 14.2 Applications Guide
iIBM SPSS Modeler 14.2 ApplicationsGuide
Note: Before using this information and the product it supports, read the general informationunder Notices on p. .This edition applies to IBM SPSS Modeler 14 and to all subsequent releases and modificationsuntil 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, 2011.U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADPSchedule Contract with IBM Corp.
PrefaceIBM SPSS Modeler is the IBM Corp. enterprise-strength data mining workbench. SPSSModeler helps organizations to improve customer and citizen relationships through an in-depthunderstanding of data. Organizations use the insight gained from SPSS Modeler to retainprofitable 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, whichleads to more powerful predictive models and shortens time-to-solution. SPSS Modeler offersmany modeling techniques, such as prediction, classification, segmentation, and associationdetection algorithms. Once models are created, IBM SPSS Modeler Solution Publisherenables their delivery enterprise-wide to decision makers or to a database.About IBM Business AnalyticsIBM Business Analytics software delivers complete, consistent and accurate information thatdecision-makers trust to improve business performance. A comprehensive portfolio of businessintelligence, predictive analytics, financial performance and strategy management, and analyticapplications provides clear, immediate and actionable insights into current performance and theability to predict future outcomes. Combined with rich industry solutions, proven practices andprofessional services, organizations of every size can drive the highest productivity, confidentlyautomate decisions and deliver better results.As part of this portfolio, IBM SPSS Predictive Analytics software helps organizations predictfuture 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 competitiveadvantage in attracting, retaining and growing customers, while reducing fraud and mitigatingrisk. By incorporating IBM SPSS software into their daily operations, organizations becomepredictive enterprises – able to direct and automate decisions to meet business goals and achievemeasurable competitive advantage. For further information or to reach a representative visithttp://www.ibm.com/spss.Technical supportTechnical support is available to maintenance customers. Customers may contact TechnicalSupport for assistance in using IBM Corp. products or for installation help for one of thesupported hardware environments. To reach Technical Support, see the IBM Corp. web siteat http://www.ibm.com/support. Be prepared to identify yourself, your organization, and yoursupport agreement when requesting assistance. Copyright IBM Corporation 1994, 2011.iii
Contents1About IBM SPSS Modeler1IBM SPSS Modeler Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1IBM SPSS Modeler Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1IBM SPSS Text Analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2IBM SPSS Modeler Documentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Application Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Demos Folder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Part I: Introduction and Getting Started2Application Examples6Demos Folder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73IBM SPSS Modeler Overview8Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Starting IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Launching 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 . . . . . . . . . .99131314IBM 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.Using the Mouse in IBM SPSS Modeler . . . . .Using Shortcut Keys . . . . . . . . . . . . . . . . . . .Printing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14151617181920212122.Automating IBM SPSS Modeler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22iv
4Introduction to Modeling24Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Browsing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Evaluating the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Scoring Records . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 405Automated Modeling for a Flag Target41Modeling Customer Response (Auto Classifier). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Generating and Comparing Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526Automated Modeling for a Continuous Target53Property Values (Auto Numeric) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53Training Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Comparing the Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Part II: Data Preparation Examples7Automated Data Preparation (ADP)62Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Comparing Model Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 688Preparing Data for Analysis (Data Audit)71Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71v
Browsing Statistics and Charts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75Handling Outliers and Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789Drug Treatments (Exploratory Graphs/C5.0)83Reading in Text Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Adding a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87Creating a Distribution Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Creating a Scatterplot. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90Creating a Web Graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Deriving a New Field. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Building a Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96Browsing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Using an Analysis Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10010 Screening Predictors (Feature Selection)102Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103Building the Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Comparing the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10811 Reducing Input Data String Length (Reclassify Node)109Reducing Input Data String Length (Reclassify). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Reclassifying the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Part III: Modeling Examples12 Modeling Customer Response (Decision List)115Historical Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Creating the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120vi
Calculating Custom Measures Using Excel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133Modifying the Excel template. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Saving the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14213 Classifying Telecommunications Customers (MultinomialLogistic Regression)144Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145Browsing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14914 Telecommunications Churn (Binomial Logistic Regression) 154Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154Browsing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16215 Forecasting Bandwidth Utilization (Time Series)169Forecasting with the Time Series Node . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169Creating the Stream. . . . . . . .Examining the Data . . . . . . . .Defining the Dates . . . . . . . . .Defining the Targets. . . . . . . .Setting the Time Intervals . . .Creating the Model . . . . . . . .Examining the Model . . . . . . .Summary . . . . . . . . . . . . . . . .Reapplying a Time Series Model . .170171175177178180182191191Retrieving the Stream . . . . . .Retrieving the Saved Model . .Generating a Modeling Node .Generating a New Model. . . .Examining the New Model . . .Summary . . . . . . . . . . . . . . . .192194195196197199vii
16 Forecasting Catalog Sales (Time Series)200Creating the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200Examining the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204Exponential Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204ARIMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21517 Making Offers to Customers (Self-Learning)216Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217Browsing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22318 Predicting Loan Defaulters (Bayesian Network)228Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228Browsing the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23319 Retraining a Model on a Monthly Basis (Bayesian Network)238Building the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238Evaluating the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24220 Retail Sales Promotion (Neural Net/C&RT)250Examining the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250Learning and Testing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25321 Condition Monitoring (Neural Net/C5.0)255Examining the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259viii
22 Classifying Telecommunications Customers (DiscriminantAnalysis)261Creating the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261Examining the Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266Stepwise Discriminant Analysis . . . . . . . . . . . . . .A Note of Caution Concerning Stepwise MethodsChecking Model Fit . . . . . . . . . . . . . . . . . . . . . . .Structure Matrix . . . . . . . . . . . . . . . . . . . . . . . . .Territorial Map. . . . . . . . . . . . . . . . . . . . . . . . . . .Classification Results. . . . . . . . . . . . . . . . . . . . . .Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26826926927027127227223 Analyzing Interval-Censored Survival Data (Generalized LinearModels)273Creating the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273Tests of Model Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279Fitting the Treatment-Only Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 281Predicted Recurrence and Survival Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282Modeling the Recurrence Probability by Period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 286Tests of Model Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292Fitting the Reduced Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294Predicted Recurrence and Survival Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 295Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29924 Using Poisson Regression to Analyze Ship Damage Rates(Generalized Linear Models)301Fitting an “Overdispersed” Poisson Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301Goodness-of-Fit Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Omnibus Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306Tests of Model Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307Fitting Alternative Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308ix
Goodness-of-Fit Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31225 Fitting a Gamma Regression to Car Insurance Claims(Generalized Linear Models)313Creating the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313Parameter Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31726 Classifying Cell Samples (SVM)318Creating the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319Examining the Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324Trying a Different Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326Comparing the Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32927 Using Cox Regression to Model Customer Time to Churn330Building a Suitable Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330Censored Cases. . . . . . . . . . . . . . . . . . . . . . . . . . . . .Categorical Variable Codings . . . . . . . . . . . . . . . . . . .Variable Selection . . . . . . . . . . . . . . . . . . . . . . . . . . .Covariate Means . . . . . . . . . . . . . . . . . . . . . . . . . . . .Survival Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Hazard Curve. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .Tracking the Expected Number of Customers Retained . . .334335336339340341342347Scoring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36628 Market Basket Analysis (Rule Induction/C5.0)367Accessing the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367Discovering Affinities in Basket Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369x
Profiling the Customer Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37329 Assessing New Vehicle Offerings (KNN)374Creating the Stream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 375Examining the Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380Predictor Space. . . . . . . . . . .Peers Chart . . . . . . . . . . . . . .Neighbor and Distance Table .Summary . . . . . . . . . . . . . . . . . . .381382385385AppendixA Notices386Bibliography389Index390xi
ChapterAbout IBM SPSS Modeler1IBM SPSS Modeler is a set of data mining tools that enable you to quickly develop predictivemodels using business expertise and deploy them into business operations to improve decisionmaking. Designed around the industry-standard CRISP-DM model, SPSS Modeler supports theentire data mining process, from data to better business results.SPSS Modeler offers a variety of modeling methods taken from machine learning, artificialintelligence, and statistics. The methods available on the Modeling palette allow you to derivenew information from your data and to develop predictive models. Each method has certainstrengths and is best suited for particular types of problems.SPSS Modeler can be purchased as a standalone product, or used in combination with SPSS ModelerServer. A number of additional options are also available, as summarized in the following sections.For more information, see s/modeler/.IBM SPSS Modeler ServerSPSS Modeler uses a client/server architecture to distribute requests for resource-intensiveoperations to powerful server software, resulting in faster performance on larger data sets.Additional products or updates beyond those listed here may also be available. For moreinformation, see s/modeler/.SPSS Modeler. SPSS Modeler is a functionally complete version of the product that is installedand run on the user’s desktop computer. It can be run in local mode as a standalone product orin distributed mode along with IBM SPSS Modeler Server for improved performance onlarge data sets.SPSS Modeler Server. SPSS Modeler Server runs continually in distributed analysis mode togetherwith one or more IBM SPSS Modeler installations, providing superior performance on largedata sets because memory-intensive operations can be done on the server without downloadingdata to the client computer. SPSS Modeler Server also provides support for SQL optimization andin-database modeling capabilities, delivering further benefits in performance and automation. Atleast one SPSS Modeler installation must be present to run an analysis.IBM SPSS Modeler OptionsThe following components and features can be separately purchased and licensed for use withSPSS Modeler. Note that additional products or updates may also become available. For moreinformation, see s/modeler/. SPSS Modeler Server access, providing improved scalability and performance on large datasets, as well as support for SQL optimization and in-database modeling capabilities. Copyright IBM Corporation 1994, 2011.1
2Chapter 1 SPSS Modeler Solution Publisher, for real-time or automated scoring outside the SPSSModeler environment. For more information, see the topic IBM SPSS Modeler SolutionPublisher in Chapter 2 in IBM SPSS Modeler 14.2 Solution Publisher. Adapters to enable deployment to IBM SPSS Collaboration and Deployment Services orthe thin-client application IBM SPSS Modeler Advantage. For more information, see thetopic Storing and Deploying IBM SPSS Collaboration and Deployment Services RepositoryObjects in Chapter 9 in IBM SPSS Modeler 14.2 User’s Guide.IBM SPSS Text AnalyticsIBM SPSS Text Analytics is a fully integrated add-on for SPSS Modeler that uses advancedlinguistic technologies and Natural Language Processing (NLP) to rapidly process a large varietyof unstructured text data, extract and organize the key concepts, and group these concepts intocategories. Extracted concepts and categories can be combined with existing structured data, suchas demographics, and applied to modeling using the full suite of IBM SPSS Modeler datamining tools to yield better and more focused decisions. The Text Mining node offers concept and category modeling, as well as an interactiveworkbench where you can perform advanced exploration of text links and clusters, createyour own categories, and refine the linguistic resource templates. A number of import formats are supported, including blogs and other web-based sources. Custom templates, libraries, and dictionaries for specific domains, such as CRM andgenomics, are also included.Note: A separate license is required to access this component. For more information, ucts/modeler/.IBM SPSS Modeler DocumentationComplete 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 ModelerSolution Publisher, as well as the Applications Guide and other supporting materials.Complete documentation for each product in PDF format is available under the \Documentationfolder on each product DVD. IBM SPSS Modeler User’s Guide. General introduction to using SPSS Modeler, including howto build data streams, handle missing values, build CLEM expressions, work with projects andreports, and package streams for deployment to IBM SPSS Collaboration and DeploymentServices, Predictive Applications, or IBM SPSS Modeler Advantage. IBM SPSS Modeler Source, Process, and Output Nodes. Descriptions of all the nodes used toread, process, and output data in different formats. Effectively this means all nodes otherthan modeling nodes. IBM
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 . Services, Predictive Applications, or IBM SPSS Modeler Advantage.
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 . Services, Predictive Applications, or IBM SPSS Modeler Advantage.
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
SPSS for Windows Version 19.0: A Basic Tutorial Linda Fiddler, California State University, Bakersfield . all you have to do to start IBM SPSS is to point to the IBM SPSS 19 icon on the desktop and double click. Then wait while IBM SPSS loads. After IBM SPSS loads, you may, depending on how IBM SPSS is set up, get a menu that .
ServicesIBM SPSS Visualization DesignerIBM SPSS Modeler Text AnalyticsIBM SPSS Text Analytics for Surveys IBM Analytical Decision ManagementIBM SPSS Modeler Social Network AnalysisIBM SPSS Analytic Server and to all subsequent releases and modifications until otherwise indicated in new editions.
Here is what the three main windows in SPSS 17.0—SPSS Data Editor, SPSS Syntax Editor, and SPSS Viewer—look like in the Windows operating environment . The SPSS Data Editor window shows the active data file. The SPSS Syntax Editor window has an SPSS program typed into it. The results of the program appear in the SPSS Viewer window.
IBM ODM IBM Operational Decision Manager IBM PMQ IBM Predictive Maintenance and Quality IBM SPSS ADM IBM SPSS Analytical Decision Management IBM SPSS C&D IBM SPSS Collaboration and Deployment Services JSON JavaScript Object Notation JVM Java Virtual Mac
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 in
Agile describes a set of guiding principles that uses iterative approach for software development Agile is a practice that helps continuous iteration of development and testing in the software development process. In this model, development and testing activities are concurrent, unlike the Waterfall model. This mindset allows more communication .
