Data Mining: Practical Machine Learning Tools and Techniques
- Ian Witten, University of Waikato, Hamilton, New Zealand.
- Eibe Frank, University of Waikato, Hamilton, New Zealand. Recipient of the 2005 ACM SIGKDD Service Award.
- Mark Hall
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.
- Published: January 2011
- Imprint: MORGAN KAUFMANN
- ISBN: 978-0-12-374856-0
Table of Contents
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