Statistical and Machine-Learning Data Mining:

Bruce Ratner, The Significant StatisticianTM, is President and Founder of DM STAT-1 Consulting, the ensample for Statistical Modeling, Analysis and Data Mining, and Machine-learning Data Mining in the DM Space. DM STAT-1 specializes in all standard statistical techniques, and methods using machine-learning/statistics algorithms, such as its patented GenIQ Model, to achieve its clients' goals – across industries including Direct and Database Marketing, Banking, Insurance, Finance, Retail, Telecommunications, Healthcare, Pharmaceutical, Publication & Circulation, Mass & Direct Advertising, Catalog Marketing, e-Commerce, Web-mining, B2B, Human Capital Management, Risk Management, and Nonprofit Fundraising. Bruce holds a doctorate in mathematics and statistics, with a concentration in multivariate statistics and response model simulation. His research interests include developing hybrid-modeling techniques, which combine traditional statistics and machine learning methods. He holds a patent for a unique application in solving the two-group classification problem with genetic programming.

Preface to Third Edition



Preface of Second Edition



Acknowledgments



Author

1. Introduction



2. Science Dealing with Data: Statistics and Data Science



3. Two Basic Data Mining Methods for Variable Assessment



4. CHAID-Based Data Mining for Paired-Variable Assessment



5. The Importance of Straight Data Simplicity and Desirability for Good Model-Building Practice



6. Symmetrizing Ranked Data: A Statistical Data Mining Method for Improving the Predictive Power of Data



7. Principal Component Analysis: A Statistical Data Mining Method for Many-Variable Assessment



8. Market Share Estimation: Data Mining for an Exceptional Case



9. The Correlation Coefficient: Its Values Range between Plus and Minus 1, or Do They?



10. Logistic Regression: The Workhorse of Response Modeling



11. Predicting Share of Wallet without Survey Data



12. Ordinary Regression: The Workhorse of Profit Modeling



13. Variable Selection Methods in Regression: Ignorable Problem, Notable Solution



14. CHAID for Interpreting a Logistic Regression Model



15. The Importance of the Regression Coefficient



16. The Average Correlation: A Statistical Data Mining Measure for Assessment of Competing Predictive Models and the Importance of the Predictor Variables



17. CHAID for Specifying a Model with Interaction Variables



18. Market Segmentation Classification Modeling with Logistic Regression



19. Market Segmentation Based on Time-Series Data Using Latent Class Analysis



20. Market Segmentation: An Easy Way to Understand the Segments



21. The Statistical Regression Model: An Easy Way to Understand the Model



22. CHAID as a Method for Filling in Missing Values



23. Model Building with Big Complete and Incomplete Data



24. Art, Science, Numbers, and Poetry



25. Identifying Your Best Customers: Descriptive, Predictive, and Look-Alike Profiling



26. Assessment of Marketing Models



27. Decile Analysis: Perspective and Performance



28. Net T-C Lift Model: Assessing the Net Effects of Test and Control Campaigns



29. Bootstrapping in Marketing: A New Approach for Validating Models



30. Validating the Logistic Regression Model: Try Bootstrapping



31. Visualization of Marketing Models: Data Mining to Uncover Innards of a Model



32. The Predictive Contribution Coefficient: A Measure of Predictive Importance



33. Regression Modeling Involves Art, Science, and Poetry, Too



34. Opening the Dataset: A Twelve-Step Program for Dataholics



35. Genetic and Statistic Regression Models: A Comparison



36. Data Reuse: A Powerful Data Mining Effect of the GenIQ Model



37. A Data Mining Method for Moderating Outliers Instead of Discarding Them



38. Overfitting: Old Problem, New Solution



39. The Importance of Straight Data: Revisited



40. The GenIQ Model: Its Definition and an Application



41. Finding the Best Variables for Marketing Models



42. Interpretation of Coefficient-Free Models



43. Text Mining: Primer, Illustration, and TXTDM Software



44. Some of My Favorite Statistical Subroutines



Index