Statistical and Machine-Learning Data Mining, 2nd Edition

The second edition of a bestseller, Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data is still the only book, to date, to distinguish between statistical data mining and machine-learning data mining. The first edition, titled Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data, contained 17 chapters of innovative and practical statistical data mining techniques. In this second edition, renamed to reflect the increased coverage of machine-learning data mining techniques, the author has completely revised, reorganized, and repositioned the original chapters and produced 14 new chapters of creative and useful machine-learning data mining techniques. In sum, the 31 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature.

The statistical data mining methods effectively consider big data for identifying structures (variables) with the appropriate predictive power in order to yield reliable and robust large-scale statistical models and analyses. In contrast, the author’s own GenIQ Model provides machine-learning solutions to common and virtually unapproachable statistical problems. GenIQ makes this possible — its utilitarian data mining features start where statistical data mining stops.

This book contains essays offering detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. They address each methodology and assign its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Table of Contents

Chapter 1. Introduction
Chapter 2. Two Basic Data Mining Methods for Variable Assessment
Chapter 3. CHAID-Based Data Mining for Paired-Variable Assessment
Chapter 4. The Importance of Straight Data: Simplicity and Desirability for Good Model-Building Practice
Chapter 5. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data
Chapter 6. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment
Chapter 7. The Correlation Coefficient: Its Values Range between Plus/Minus 1, or Do They?
Chapter 8. Logistic Regression: The Workhorse of Response Modeling
Chapter 9. Ordinary Regression: The Workhorse of Profit Modeling
Chapter 10. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution
Chapter 11. CHAID for Interpreting a Logistic Regression Model
Chapter 12. The Importance of the Regression Coefficient
Chapter 13. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables
Chapter 14. CHAID for Specifying a Model with Interaction Variables
Chapter 15. Market Segmentation Classification Modeling with Logistic Regression
Chapter 16. CHAID as a Method for Filling in Missing Values
Chapter 17. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling
Chapter 18. Assessment of Marketing Models
Chapter 19. Bootstrapping in Marketing: A New Approach for Validating Models
Chapter 20. Validating the Logistic Regression Model: Try Bootstrappin
Chapter 21. Visualization of Marketing ModelsData Mining to Uncover Innards of a Model
Chapter 22. The Predictive Contribution Coefficient: A Measure of Predictive Importance
Chapter 23. Regression Modeling Involves Art, Science, and Poetry, Too
Chapter 24. Genetic and Statistic Regression Models: A Comparison
Chapter 25. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model
Chapter 26. A Data Mining Method for Moderating Outliers Instead of Discarding Them
Chapter 27. Overfitting: Old Problem, New Solution
Chapter 28. The Importance of Straight Data: Revisited
Chapter 29. The GenIQ Model: Its Definition and an Application
Chapter 30. Finding the Best Variables for Marketing Models
Chapter 31. Interpretation of Coefficient-Free Models