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Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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Binding: Paperback
Dewey Decimal Number: 006.3
EAN: 9780120884070
Edition: 2
ISBN: 0120884070
Label: Morgan Kaufmann
Manufacturer: Morgan Kaufmann
Number Of Items: 1
Number Of Pages: 560
Publication Date: June 08, 2005
Publisher: Morgan Kaufmann
Release Date: June 10, 2005
Studio: Morgan Kaufmann
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Editorial Review:
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
Average Rating: 
Rating:  - Not particularly useful
The material is very superficially laid out and for a book with the word "Practical" in the sub-title it contains almost no practical examples of data mining.
Rating:  - Thorough, well-written, and crystal-clear explanations.
Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books.
Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly becoming a standard, invaluable research toolbox for many.
Rating:  - A little too wordy for my tastes, but good
This book was pretty good. I have to admit that for the first hundred or so pages, I was feeling very impatient. All of that information could have been conveyed in about 25 pages, and been much easier to read. But there are some very good examples in here, and it is worth reading. If you are looking for something more technical, try "Pattern Recognition and Machine Learning", by Christopher M. Bishop or "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman.
Rating:  - Superficial
This book reminds me of the programming books by Deitel&Deitel. It's wordy and superficial, making lots of people feel like they understand the subject. Unfortunately, it takes *much* more than what's in this book to really understand Data Mining. Compare this book to the book by Hastie, Friedman and Tibshiranie, which really goes into the statistics involved in Data Mining.
There is no magic: real Data Mining needs lots of Statistics. You can learn to use Weka, but in order to do real work ... Read More
Rating: - Awesome
I am very happy with amazon purchases as they always come quick, as described. I love the free supersavings shipping program. Prices are charged in the middle (not the chepest, not the highest) but I know I can always rely on Amazon! Every time I have something to buy online, I go to Amazon.
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