Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
As with any burgeoning technology that enjoys commercial attention, the use of data mining 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 book 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; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
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Doing it again
Training and testing learning schemes
Clustering and association rules
Unsupervised instance filters
The Knowledge Flow interface
Analyzing the results
Other editions - View all
Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten,Eibe Frank,Mark A. Hall
Limited preview - 2011
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