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

By: Contributor(s): Material type: TextTextSeries: Morgan Kaufmann series in data management systemsPublication details: Burlington, MA : Morgan Kaufmann, c2011.Edition: 3rd edDescription: xxxiii, 629 p. : ill. ; 24 cmISBN:
  • 9780123748560 (pbk.)
  • 0123748569 (pbk.)
Subject(s): DDC classification:
  • 006.3/12 22
LOC classification:
  • QA76. WIT 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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Holdings
Item type Current library Call number Copy number Status Barcode
Books in General collection Books in General collection Mzuzu University Library and Learning Resources Centre QA 75.9 WIT 2011 (Browse shelf(Opens below)) 030910 Available mZulm-030910
Books in General collection Books in General collection Mzuzu University Library and Learning Resources Centre QA 75.9 WIT 2011 (Browse shelf(Opens below)) 030909 Available mZulm-030909

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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