Data mining : practical machine learning tools and techniques /

by Witten, Ian H
Additional authors: Frank, Eibe | Hall, Mark A
Series: [Morgan Kaufmann series in data management systems] Edition statement:3. ed. Published by : Morgan Kaufmann (Burlington, MA) Physical details: xxxiii, 629 p. ill. 24 cm ISBN:9780123748560 (pbk.). Year: 2011
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Includes bibliographical references and index

PART I: Machine Learning Tools and Techniques. 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

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