Data mining: practical machine learning tools and techniques/
| Main Author: | |
|---|---|
| Other Authors: | , |
| Format: | Book |
| Language: | English |
| Published: |
Burlington, Massachusetts:
Morgan Kaufmann Publisher,
c2011
|
| Edition: | 3rd ed. |
| Series: | [Morgan Kaufmann series in data management systems]
|
| Subjects: |
Table of 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.