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Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects

Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods

Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization


Description

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.



Readership
Information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals, as well as professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise.

Quotes

´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


Contents
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Author Information
By Ian H. Witten, University of Waikato, Hamilton, New Zealand.; Eibe Frank, University of Waikato, Hamilton, New Zealand. Recipient of the 2005 ACM SIGKDD Service Award. and Mark A. Hall