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Data Mining: Practical Machine Learning Tools and Techniques Paperback – Jan 6 2011

4.2 out of 5 stars 9 customer reviews

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Product details

  • Paperback: 664 pages
  • Publisher: Morgan Kaufmann; 3 edition (Jan. 20 2011)
  • Language: English
  • ISBN-10: 0123748569
  • ISBN-13: 978-0123748560
  • Product Dimensions: 19 x 3.8 x 23.5 cm
  • Shipping Weight: 1.4 Kg
  • Average Customer Review: 4.2 out of 5 stars 9 customer reviews
  • Amazon Bestsellers Rank: #158,899 in Books (See Top 100 in Books)
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Product description

Review

"The authors provide enough theory to enable practical application, and it is this practical focus that separates this book from most, if not all, other books on this subject."– Dorian Pyle, Director of Modeling at Numetrics and an internationally known author of Data Preparation for Data Mining (Morgan Kaufmann, 1999) and Business Modeling for Data Mining (Morgan Kaufmann, 2003)

"This book would be a strong contender for a technical data mining course. It is one of the best of its kind."– Herb Edelstein, Principal, Data Mining Consultant, Two Crows Consulting.

"It is certainly one of my favorite data mining books in my library"– Tom Breur, Principal, XLNT Consulting, Tilburg, The Netherlands

From the Back Cover

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.

Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.

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