Data Mining, 3rd Edition

03/27/2013 · No Responses

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.

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

Table of Contents
Part I: Introduction to Data Mining
Chapter 1. What’s It All About?
Chapter 2. Input: Concepts, Instances, Attributes
Chapter 3. Output: Knowledge Representation
Chapter 4. Algorithms: The Basic Methods
Chapter 5. Credibility: Evaluating What’s Been Learned

Part II: Advanced Data Mining
Chapter 6. Implementations: Real Machine Learning Schemes
Chapter 7. Data Transformation
Chapter 8. Ensemble Learning
Chapter 9. Moving On: Applications and Beyond

Part III: The Weka Data MiningWorkbench
Chapter 10. Introduction to Weka
Chapter 11. The Explorer
Chapter 12. The Knowledge Flow Interface
Chapter 13. The Experimenter
Chapter 14. The Command-Line Interface
Chapter 15. Embedded Machine Learning
Chapter 16. Writing New Learning Schemes
Chapter 17. Tutorial Exercises for the Weka Explorer