Data Mining – Ian H. Witten, Frank Eibe – 2nd Edition

As with any burgeoning technology that enjoys commercial attention, the use of is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This describes these techniques and shows they work.

The book is a major revision of the edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

1. What’s it all about?
2. : , instances, attributes
3. Output: Knowledge representation
4. Algorithms: The basic methods
5. Credibility: Evaluating what’s been learned
6. Implementations: Real machine learning schemes
7. Transformations: the input and output
8. Moving on: Extensions and
9. Introduction to Weka
10. The Explorer
11. The Knowledge Flow interface
12. The Experimenter
13. The command-line interface
14. Embedded machine learning
15. Writing new learning schemes

Title: Data Mining: Practical Machine Learning Tools and Techniques
Author: Frank Eibe / Ian H. Witten
Edition: 2nd Edition
ISBN: 9780080477022
Type: eBook
Language: English

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