Skip to content

Data Mining Practical Machine Learning Tools and Techniques

Spend $50 to get a free DVD!

ISBN-10: 0120884070

ISBN-13: 9780120884070

Edition: 2nd 2005 (Revised)

Authors: Ian H. Witten, Eibe Frank

List price: $75.95
Blue ribbon 30 day, 100% satisfaction guarantee!
Out of stock
what's this?
Rush Rewards U
Members Receive:
Carrot Coin icon
XP icon
You have reached 400 XP and carrot coins. That is the daily max!

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…    
Customers also bought

Book details

List price: $75.95
Edition: 2nd
Copyright year: 2005
Publisher: Elsevier Science & Technology Books
Publication date: 6/8/2005
Binding: Paperback
Pages: 560
Size: 7.50" wide x 9.25" long x 1.50" tall
Weight: 2.486
Language: English

Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.

Whats it all about?
Input
Concepts, instances, attributes
Output
Knowledge representation
Algorithms
The basic methods
Credibility
Evaluating whats been learned
Implementations
Real machine learning schemes
Transformations
Engineering the input and output
Moving on
Extensions and applications
The Weka machine learning workbench
Introduction to Weka
The Explorer
The Knowledge Flow interface
The Experimenter
The command-line interface
Embedded machine learning
Writing new learning schemes
References
Index