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Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall.

By: Witten, I. H. (Ian H.).
Contributor(s): Frank, Eibe | Hall, Mark A.
Material type: materialTypeLabelBookSeries: Morgan Kaufmann series in data management systems: Publisher: Burlington, MA : Morgan Kaufmann, c2011Edition: 3rd ed.Description: xxxiii, 629 p. : ill. ; 24 cm.ISBN: 9780123748560 (pbk.); 0123748569 (pbk.).Subject(s): Data miningDDC classification: 006.3/12
Contents:
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 -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 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.
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Item type Current location Call number Status Date due
Lending Books Lending Books Sri Lanka Institute of Development Administration
006.3/12WIT (Browse shelf) Available

Includes bibliographical references (p. 587-605) and index.

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 -- Part II. Advanced Data Mining: 6. Implementations: real machine learning schemes; 7. Data transformation; 8. Ensemble learning; 9. Moving on: applications and beyond -- Part III. The Weka Data MiningWorkbench: 10. Introduction to Weka; 11. The explorer -- 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.

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