Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) |  | Authors: Ian H. Witten, Eibe Frank
Publisher: Morgan Kaufmann
List Price: $65.95
Buy New: $37.04
as of 11/22/2009 02:33 CST details
You Save: $28.91 (44%)
New (41) Used (27) from $27.69
Seller: student_friendly_books
Rating:  31 reviews
Sales Rank: 22869
Media: Paperback
Edition: 2
Pages: 560
Number Of Items: 1
Shipping Weight (lbs): 1.3
Dimensions (in): 9.9 x 7.6 x 0.7
ISBN: 0120884070
Dewey Decimal Number: 006.3
EAN: 9780120884070
ASIN: 0120884070
Publication Date: June 22, 2005
Availability: Usually ships in 1-2 business days
| |
| Also Available In:
|
| Similar Items:
| |
| Editorial Reviews:
Product Description
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; 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.
* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface
|
| Customer Reviews:
Showing reviews 1-5 of 31
Researcher rates text October 19, 2009
Dave
This book provides a good overview of the uses and techniques of data mining, then drills steadily into more detail, allow the reader to determine what level of detail he is interested in learning. Practical application of the techniques via WEKA re-enforce the learning possible through the use of this book.
I am still looking for an equivalent approach for text mining, which this book addresses at a high level.
A data mining Book for Open source tools May 8, 2009
Senni Alberto (Bologna, BO Italy)
Data Mining Sec Edition is a good book with a good support for related open source Weka system. You can test and exercize with no cost with data mining tecnique.
Good work
 Useful addition to your shelf January 14, 2009
Stephen Lowe (Timaru, South Canterbury New Zealand)
2 out of 2 found this review helpful
This title is a most useful addition to your data mining collection, especially if you plan to do some practical experimentation with the Open Source WEKA software it describes. You do not need a degree in maths or statistics to work with this text, conversely don't expect detailed line-by-line explanations of each algorithm either. If you want that open up the Java code in the WEKA software. I note in the other reviews comments about the practical nature of the book: the authors are at Waikato University in the heart of New Zealand's dairy country, and it is this practical agricultural application that has driven the research behind the text and the software, as opposed to books written by authors working in the cloistered mathematics departments of the major universities. Recommended for serious students new or not new to the subject.
A book to understand the subject January 13, 2009
Claudio Luis Sturla (Buenos Aires, Argentina)
2 out of 2 found this review helpful
It's the best book I know because it explains the topics in a very didactic fashion and the book refers to a computer program easily available (Weka, from the University of Waikato of New Zealand)because is Open Source.
 Not very user-friendly, too much emphasis on Weka language January 7, 2009
Ada
1 out of 1 found this review helpful
This book was used as one of the two textbooks in a graduate school database course. It is hard to follow and places too much emphasis on the Weka data mining language (the authors developed Weka). As a data mining beginner, I had to consult several other data mining references in addition to this book.
Showing reviews 1-5 of 31
|
|
|
|
