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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: TextTextPublisher: Amsterdam, Netherlands : Elsevier/Morgan Kaufmann, [2011]Copyright date: ©2011Edition: Third editionDescription: xxxiii, 629 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780123748560
  • 0123748569
Subject(s): DDC classification:
  • 006.3/12 22
LOC classification:
  • QA76.9 WIT
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.
Item type: Books
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Holdings
Item type Current library Collection Call number Copy number Status Date due Barcode
Books Books GSU Library Epoch General Stacks NFIC QA76.9WIT (Browse shelf(Opens below)) 1 Available 50000005011
Books Books GSU Library Epoch General Stacks NFIC QA76.9WIT (Browse shelf(Opens below)) 2 Available 50000005012

Includes bibliographical references (pages 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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