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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: New Delhi : Elsevier, Morgan Kaufmann, c2011.Edition: 3rd edDescription: xxxiii, 629 p. : ill. ; 24 cmISBN:
  • 9789380501864
Subject(s): DDC classification:
  • 006.312 22 WIT
LOC classification:
  • QA76.9.D343 W58 2011
Contents:
Part I. Machine Learning Tools and Techniques: 1. What's it 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 Mining Workbench: 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 Status Date due Barcode Item holds
Books Books Learning Resource Centre 006.312 WIT (Browse shelf(Opens below)) Available 4906
Total holds: 0

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

Part I. Machine Learning Tools and Techniques: 1. What's it 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 Mining Workbench: 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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