Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, 2/e (Hardcover)

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.

這本暢銷書的第二版，《統計與機器學習數據挖掘：更好的預測建模和大數據分析技術》，仍然是迄今為止唯一一本區分統計數據挖掘和機器學習數據挖掘的書籍。第一版名為《數據庫營銷的統計建模和分析：挖掘大數據的有效技術》，包含了17章創新且實用的統計數據挖掘技術。在這第二版中，為了反映對機器學習數據挖掘技術的增加覆蓋範圍，作者完全修訂、重新組織和重新定位了原有章節，並增加了14個新的創造性和有用的機器學習數據挖掘技術章節。總之，這本書的31章簡單而深入的量化技術使其在數據挖掘文獻領域獨一無二。

統計數據挖掘方法有效地考慮了大數據，以識別具有適當預測能力的結構（變量），從而產生可靠且強大的大規模統計模型和分析。相比之下，作者自己的GenIQ模型提供了機器學習解決常見且幾乎無法解決的統計問題的解決方案。GenIQ使這成為可能-其實用的數據挖掘功能從統計數據挖掘停止的地方開始。

這本書包含了詳細的背景、討論和具體方法的示例，用於解決大數據預測建模和分析中最常遇到的問題。它們涵蓋了每種方法並將其應用於特定類型的問題。為了更好地幫助讀者理解，本書深入討論了預測建模和分析的基本方法。雖然此類概述以前已嘗試過，但這種方法提供了一種真正細節入微、逐步方法，無論是新手還是專家都可以享受其中的遊戲。