Data mining : practical machine learning tools and techniques.
By: Witten, I. H
Title By: Frank, Eibe | Hall, Mark A
Material type: BookSeries: Morgan Kaufmann series in data management systems.Publisher: Burlington, MA : Morgan Kaufmann, c2011.Edition: 3rd ed. Ian H. Witten, Eibe Frank, Mark A. Hall.Description: xxxiii, 629 p. : ill. ; 24 cm.ISBN: 9780123748560 (pbk.); 0123748569 (pbk.)Subject(s): Data miningDDC classification: 006.3/12 Online resources: Location MapItem type | Home library | Call number | Status | Date due | Barcode | Item holds |
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REGULAR | University of Wollongong in Dubai Main Collection | 006.312 WI DA (Browse shelf) | Available | T0043656 |
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006.312 VE US Using openrefine : | 006.312 VE US Using openrefine : | 006.312 WA IN Innovative techniques and applications of entity resolution / | 006.312 WI DA Data mining : | 006.312 WI DA Data mining : | 006.312 WI DA Data mining with Rattle and R : | 006.312 WI DA Data mining with Rattle and R : |
The third edition of this practical guide to machine learning and data mining is fully updated to account for technological advances since its previous printing in 2005 and is now even more closely aligned with the use of the Weka open source machine learning, data mining and data modeling application. Beginning with an introduction to data mining, the volume explores basic inputs, outputs and algorithms, the implementation of machine learning schemes and in-depth exploration of the many uses of the Weka data analysis software. Numerous illustration, tables and equations are included throughout and additional resources are available through a companion website. Witten, Frank and Hall are academics with the department of computer science at the University of Waikato, New Zealand, the home of the Weka software project.
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.