Bruce Ratner

December 19, 2011
by CRC Press

Reference
- 542 Pages
- 183 B/W Illustrations

ISBN 9781439860915 - CAT# K12803

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- Distinguishes between statistical data mining and machine-learning data mining techniques, leading
*to better predictive modeling and analysis of big data*Illustrates the power of machine-learning data mining that starts where statistical data mining stops - Addresses common problems with more powerful and reliable alternative data-mining solutions than those commonly accepted
- Explores uncommon problems for which there are no universally acceptable solutions and introduces creative and robust solutions
- Discusses everyday statistical concepts to show the hidden assumptions not every statistician/data analyst knows—underlining the importance of having good statistical practice

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.

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