ISBN0321321367

I decided to start with this book as I think it is the most convenient to start in the data mining field. One big advantage of the book is the way data mining techniques are explained. It is mainly based on textual and graphical explanations. There is little equations, only what is necessary to implement the algorithms.

This book widely cover areas such as data preparation and understanding, classification, anomaly detection, association analysis and clusering. Although the book has a strong emphasis on the two last ones, nearly all standard data mining techniques are at least briefly discussed. However, this book does only have a fiew pages about kernel methods for example. Indeed, it is normal, as kernel methods are more suitable for machine learning (I mean making prediction) than data mining (I mean looking for description).

Therefore, this book is:

* able to explain data mining without thousands of equations
* a good way to start with data mining
* covering nearly all standard data mining techniques
* focused on association analysis and clustering

and it is not:

* a good book for kernel methods and other advanced techniques
* written in the statistical nor in the database perspective

My comment: if you are in the data mining field and not comming from mathematics or databases, then you really should buy this book.

I've just made it through the first 6 chapters of the book so far but I really enjoy this book so far. This book is terrific at introducing this material in an easy-to-understand manner. I've found myself using to supplement my machine learning textbook when more thorough explanations are needed. The section on support vectors was the easiest to grasp from about a dozen references I had on hand. I've seen a few typos here and there but I suppose that's expected from a first edition.

This book gives an excellent overview of data mining techniques, and gives thorough information about machine learning fundamentals. The key advantages of this book are its clean structure and high quality content and illustrations.

This book is amazingly well written. Everything is explained in a very clear and to-the-point style. The book can be read from front to back or used as a reference book. It contains countless diagrams and the structure of the content is immediately apparent.

The book covers a lot of the important aspects of data mining. It provides algorithms and techniques for classification, clustering, association analysis, and anomaly detection. Every algorithm is not only formally stated, but also explained in a way that conveys intuition.

I only wish other authors also wrote books this way.

I agree with the other reviews: The book is amazingly well-written, and the two chapters on cluster analysis are second-to-none;
Though I am particularly enthusiastic about this book, I believe that it cannot deserve 5 stars, for the following reasons:
- Kernel methods: like most books on this subject, the authors do not explain how to choose the most appropriate kernel(s)
- Cluster analysis: No examples of time-series
- Fully worked-out real-world examples are missing
- no solutions to the exercises

If not possible to wait for a second edition, do not hesitate, this is definitely the best introduction you can find.