Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) - Softcover

Witten, Ian H.; Frank, Eibe

3.9

775 ratings by Goodreads

9780120884070: Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)

Softcover

ISBN 10: 0120884070 ISBN 13: 9780120884070

Publisher: Morgan Kaufmann, 2005

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Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references.

The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more.

This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses.

Algorithmic methods at the heart of successful data mining―including tried and true techniques as well as leading edge methods
Performance improvement techniques that work by transforming the input or output

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Book Description:

Highly anticipated second edition of the highly-acclaimed reference on data mining and machine learning.

From the Back Cover:

This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate.

If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start.
--From the foreword by Jim Gray, Microsoft Research

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you’ll find:

+ Algorithmic methods at the heart of successful data mining―including tried and true techniques as well as leading edge methods;
+ Performance improvement techniques that work by transforming the input or output

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