Data Mining with SPSS Modeler

1. Front Matter

   Pages i-xv

   PDF

2. Introduction

   • Tilo Wendler, Sören Gröttrup

   Pages 1-22

3. Basic Functions of the SPSS Modeler

   • Tilo Wendler, Sören Gröttrup

   Pages 23-189

4. Univariate Statistics

   • Tilo Wendler, Sören Gröttrup

   Pages 191-305

5. Multivariate Statistics

   • Tilo Wendler, Sören Gröttrup

   Pages 307-366

6. Regression Models

   • Tilo Wendler, Sören Gröttrup

   Pages 367-546

7. Factor Analysis

   • Tilo Wendler, Sören Gröttrup

   Pages 547-622

8. Cluster Analysis

   • Tilo Wendler, Sören Gröttrup

   Pages 623-752

9. Classification Models

   • Tilo Wendler, Sören Gröttrup

   Pages 753-1088

10. Using R with the Modeler

    • Tilo Wendler, Sören Gröttrup

    Pages 1089-1146

11. Imbalanced Data and Resampling Techniques

    • Tilo Wendler, Sören Gröttrup

    Pages 1147-1191

12. Case Study: Fault Detection in Semiconductor Manufacturing Process

    • Tilo Wendler, Sören Gröttrup

    Pages 1193-1248

13. Appendix

    • Tilo Wendler, Sören Gröttrup

    Pages 1249-1274

Now in its second edition, this textbook introduces readers to the IBM SPSS Modeler and guides them through data mining processes and relevant statistical methods. Focusing on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs, it also features a variety of exercises and solutions, as well as an accompanying website with data sets and SPSS Modeler streams. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice. This revised and updated second edition includes a new chapter on imbalanced data and resampling techniques as well as an extensive case study on the cross-industry standard process for data mining.

• IBM SPSS Modeler
• data mining
• statistical software
• predictive analytics
• statistical methods
• exercises, solutions, case studies
• imbalanced data
• resampling techniques
• cross-industry standard process for data mining
• R and SPSS Modeler
• multivariate statistics
• regression models
• factor analysis
• cluster analysis
• classification models
• data mining process
• 62-07, 62Hxx, 62Pxx, 62-01

• University of Applied Sciences HTW Berlin, Berlin, Germany

  Tilo Wendler

• Stuttgart, Germany

  Sören Gröttrup

Dr. Tilo Wendler is a Professor at the University of Applied Sciences HTW Berlin, Germany. He studied mathematics, physics and business information technology. In his doctoral thesis, he examined determinants of user expectations in using information technology. He is also interested in applying complex statistical methods in the banking sector, especially in the field of rating methods. He has been teaching business statistics and data mining for ten years.

Dr. Sören Gröttrup is a Professor of Machine Learning and Statistics at the Technische Hochschule Ingolstadt, Germany.  After studying mathematics and computer science, he was awarded a Ph.D. for his research on stochastic models with biological applications. Alongside his doctoral studies, he worked as a data analyst, analyzing genomic data at a research institute. For many years, he worked as a data scientist in the airline and data-driven marketing business, and advised various companies on digitalization and machine learning projects.

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