Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 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.

The book is targeted at 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. The book will also be useful for 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.

• 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

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.

PART I: Introduction to Data Mining Ch 1 What's It All About? Ch 2 Input: Concepts, Instances, Attributes Ch 3 Output: Knowledge Representation Ch 4 Algorithms: The Basic Methods Ch 5 Credibility: Evaluating What's Been Learned PART II: Advanced Data Mining

Ch 6 Implementations: Real Machine Learning Schemes Ch 7 Data Transformation Ch 8 Ensemble Learning Ch 9 Moving On: Applications and Beyond PART III: The Weka Data MiningWorkbench Ch 10 Introduction to Weka Ch 11 The Explorer Ch 12 The Knowledge Flow Interface Ch 13 The Experimenter Ch 14 The Command-Line Interface Ch 15 Embedded Machine Learning Ch 16 Writing New Learning Schemes Ch 17 Tutorial Exercises for the Weka Explorer