Data mining
Subtitle: practical machine learning tools and techniques
By: Ian H. Witten, Eibe Frank, Mark A. Hall
Contributors: Frank, Eibe; Hall, Mark A.
Edition: 3rd ed.
Series: [Morgan Kaufmann series in data management systems]; Morgan Kaufmann series in data management systems
Publisher: Morgan Kaufmann
Published: 2011
Place of publication: Burlington, MA
Language: eng
Number of pages: 629
ISBN-13: 9780123748560
Subjects: Data mining
Notes: Includes bibliographical references (p. 587-605) and index.
Table of contents: title: Part I. Machine Learning Tools and Techniques: 1. What's iIt all about?; 2. Input: concepts, instances, and attributes; 3. Output: knowledge representation; 4. Algorithms: the basic methods; 5. Credibility: evaluating what's been learned; title: Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond; title: Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer; title: 12. The knowledge flow interface; 13. The experimenter; 14 The command-line interface; 15. Embedded machine learning; 16. Writing new learning schemes; 17. Tutorial exercises for the weka explorer.