Table of contents for Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank.

Bibliographic record and links to related information available from the Library of Congress catalog

Information from electronic data provided by the publisher. May be incomplete or contain other coding.


1. What’s it all about?
2. Input: Concepts, instances, attributes
3. Output: Knowledge representation
4. Algorithms: The basic methods
5. Credibility: Evaluating what’s been learned
6. Implementations: Real machine learning schemes
7. Transformations: Engineering the input and output
8. Moving on: Extensions and applications

Part II: The Weka machine learning workbench

9. Introduction to Weka
10. The Explorer
11. The Knowledge Flow interface
12. The Experimenter
13. The command-line interface
14. Embedded machine learning
15. Writing new learning schemes


Library of Congress subject headings for this publication:
Data mining.