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

Description

As with any burgeoning 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 , 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 how they work.

The book is a major revision of the first 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 machine learning workbench, which now features an interactive interface; comprehensive information on neural ; a new section on Bayesian ; plus much more.

Table of Content

1. What’s it all about?
2. Input: Concepts, 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: Engineering the input and output
8. Moving on: Extensions and applications
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

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