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

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:
  • QA76.9.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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Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
Short Loan for 1 day Short Loan for 1 day UOE Main Library Short Loan Area QA76.9.D343 W58 2011 (Browse shelf(Opens below)) 20150198 Available 20150198
Short Loan for 1 day Short Loan for 1 day UOE Main Library Short Loan Area QA76.9.D343 W58 2011 (Browse shelf(Opens below)) 20150197 Available 20150197
Short Loan for 1 day Short Loan for 1 day UOE Main Library Short Loan Area QA76.9.D343 W58 2011 (Browse shelf(Opens below)) 20150196 Available 20150196
Total holds: 0

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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