Data mining :
Witten, I. H.
Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. - 3rd ed. - New Delhi : Elsevier, Morgan Kaufmann, c2011. - xxxiii, 629 p. : ill. ; 24 cm. - [Morgan Kaufmann series in data management systems] . - Morgan Kaufmann series in data management systems. .
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.
9789380501864
2010039827
Data mining.
QA76.9.D343 / W58 2011
006.312 / WIT
Data mining : practical machine learning tools and techniques / Ian H. Witten, Eibe Frank, Mark A. Hall. - 3rd ed. - New Delhi : Elsevier, Morgan Kaufmann, c2011. - xxxiii, 629 p. : ill. ; 24 cm. - [Morgan Kaufmann series in data management systems] . - Morgan Kaufmann series in data management systems. .
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.
9789380501864
2010039827
Data mining.
QA76.9.D343 / W58 2011
006.312 / WIT