Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, 2/e (Hardcover)
暫譯: 統計與機器學習資料探勘:提升大數據預測模型與分析的技術,第二版(精裝本)
Bruce Ratner
- 出版商: CRC
- 出版日期: 2011-12-19
- 售價: $2,930
- 貴賓價: 9.5 折 $2,784
- 語言: 英文
- 頁數: 542
- 裝訂: Hardcover
- ISBN: 1439860912
- ISBN-13: 9781439860915
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相關分類:
大數據 Big-data、Data-mining、Machine Learning
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商品描述
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.
商品描述(中文翻譯)
第二版的暢銷書《統計與機器學習資料探勘:改善預測建模與大數據分析的技術》至今仍是唯一一本區分統計資料探勘與機器學習資料探勘的書籍。第一版名為《資料庫行銷的統計建模與分析:挖掘大數據的有效技術》,包含17章創新且實用的統計資料探勘技術。在這一版中,書名已更改以反映機器學習資料探勘技術的增加覆蓋範圍,作者對原有章節進行了全面的修訂、重組和重新定位,並新增了14章創新且有用的機器學習資料探勘技術。總而言之,這31章簡單卻深刻的定量技術使本書在資料探勘文獻中獨樹一幟。
統計資料探勘方法有效地考慮大數據,以識別具有適當預測能力的結構(變數),從而產生可靠且穩健的大規模統計模型和分析。相對而言,作者自己的GenIQ模型提供了針對常見且幾乎無法接觸的統計問題的機器學習解決方案。GenIQ使這一切成為可能——其實用的資料探勘功能從統計資料探勘停止的地方開始。
本書包含多篇文章,提供詳細的背景、討論和具體方法的說明,以解決在預測建模和大數據分析中最常遇到的問題。這些文章針對每種方法論,並將其應用分配給特定類型的問題。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論。雖然之前曾嘗試過這類概述,但這種方法提供了一種真正的細緻、逐步的方法,無論是初學者還是該領域的專家都能享受其中的樂趣。