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Series: The Morgan Kaufmann Series in Data Management Systems
Paperback: 664 pages
Publisher: Morgan Kaufmann; 3 edition (January, 2011)
Language: EnglishISBN-10: 0123748569
Product Dimensions: 9.2 x 7.5 x 1.7 inches
Shipping Weight: 3 pounds
*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
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.
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.
Table of Contents
Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition
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