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

By: Witten, I. H. (Ian H.) [author.]
Contributor(s): Frank, Eibe [author.] | Hall, Mark A [author.]
Series: Morgan Kaufmann series in data management systems: Publisher: Burlington : Morgan Kaufman Publishers, c2011Edition: Third editionDescription: xxxiii, 629 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780123748560 (pbk.); 0123748569 (pbk.)Subject(s): Data miningDDC classification: 006.3/12 LOC classification: QA76.9.D343 | W58 2011
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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SUBJECT REFERENCE 		006.312 W784 2011 (Browse shelf) Available CITU-CL-42631
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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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