The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.

This book can be read and understood by anyone who has had a decent basic course in statistics or or in pattern recognition. It alerts the reader to potential pitfalls in using a particular data mining procedure. It also clearly describes essential differences between procedures. Examples from real data are clear and integrated with the text.

This is not a "cookbook" that teaches you keystroke by keystroke how to implement an algorithm. Instead this book is a guide in understanding the fundamentals behind each procedure (as good as possible assuming low level math skills), and hints on interpetation of output, especially limits to interpretation. It is very well written and can stand alone as a guide or serve as a testbook in a data mining class.

Now if they would just write a book on bayesian decision-making in the same way.

20 of 22 found the following review helpful:


Excellent introductory text on data mining  Apr 29, 2003
This is an excellent book for students in engineering and computer science who would like an introductory and statistical treatment of data mining. It has much more statistical content than other widely-used data mining texts such as those by Han and Kamber or Witten and Frank. And it is better suited to senior undergraduate or first-year graduate students in CS and EE than the text by Hastie and colleagues, since it has broader coverage of data mining topics and a more tutorial-style introduction to the basic principles of inference from data.

The coverage emphasizes breadth rather than depth and this works well for an introductory text. Numerous and extensive references are provided for further reading. The layout of the book is interesting, proceeding from data visualization (often ignored in many data mining books) through general principles of inference and algorithms, to more specific techniques in classification and regression. If you are interested in data mining and would like a statistically-motivated introduction, then this is the book to start with.

15 of 16 found the following review helpful:


Excellent Text!  Jan 16, 2002
This book fills a gaping hole in the literature on data mining. It is not a machine learning text repackaged as a data mining text, it is not a business/data mining hype book and it is not simply a collection of papers or algorithms. As the title states, it is a text that describes the _principles_ of data mining. Most importantly, it provides a clear description of the statistical foundations for data mining in a manner that is accessible to computer science students. It is an excellent choice for an upper division or graduate course in data mining.

8 of 9 found the following review helpful:


A welcome addition to data mining  Dec 18, 2001
This is a welcome addition to the canon of books on data mining. It is an interdisciplinary book, drawing together the differing views of the statistician and the computer scientist, but with an emphasis on the principles underlying data mining. Many data mining books are written from a specialist computer scientist's viewpoint or from a similarly specialist business users one. In the former, the emphasis tends to be on algorithms and computational efficiency, while in the latter business applications of a small number of techniques are the main thrust.

Data mining requires an understanding of concepts from statistics and computer science, and the authors illustrate this with many examples. The first third of the book covers fundamentals of data analysis, which is appropriate, because some deep statistical ideas can arise in data mining problems, and those without training in statistics may not be aware of their consequences. The next third covers components of data mining algorithms, and the final part of the book draws the two strands together in a unified whole, with descriptions of typical data mining tasks and suitable algorithms.

It is a well written and easy to understand book, and will be an ideal reference for researchers and practitioners from either discipline, who may be seeking a greater understanding of the other.

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