computers and internet

Witten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines?

There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles.

The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at).

For what they have undertaken, they have performed admirably, and I would highly recommend this book.

It's difficult to get interesting
literature related to this theme.

On the one hand there are some books written for managers, on the other hand there are some pretty mathematical books for academics. But this book is the best mix. You get an introduction to data mining and learn step by step from the basics up to the hard algorithm stuff with nice examples.
There is a clear theme structure, and the deep technical sections are marked, so you can read what you are most interested in. The book describes not only one algorithm, but a lot of them and discusses plusses and minuses. Where it's necessary it uses simple diagrams to illustrate something, not so much that it looks like they want to fill the pages, like in other books. Best of all, the algorithms are implemented as an
open source java software named "weka". This is my state of the art data mining tool.
You can see the algorithms working and use the implementations for your ideas (like me). If you are hungry to learn more
about one or the other thing, the book provides a literature list.

For me this book was one of the best books in the last years, because it provides the best mix and gives you a fast but deep view in this theme.

I've been working with "big name software" for some years, but when I joined the institution I work now and no tools where available I begun my quest for an open source tool that could help me build statistical models applied to real business problems.

As a result of this quest I found the WEKA data mining software on the Internet (you can find it on www.cs.waikato.ac.nz/~ml/weka/) and that nice piece of software leaded me to this book.

This book is EXCELLENT and I am giving 5 *five* stars to it as it helped me understanding the whole process of datamining: from loading the data to building the model.

I've read some reviews and I think some of them are not fair (particularly one that says that this book have "just words with no relation or sense at all").. THIS BOOK IS REALLY WELL WRITTEN but you have to read it slowly: As when you study something.

Buy this book (*don't forget to download the software*) and I am totally sure that you will be producing and using models in a week.

Can't imagine that some weeks ago

Cheers,