Customer Reviews

Principles of Data Mining (Adaptive Computation and Machine Learning)

5 star:		(8)
4 star:		(4)
3 star:		(1)
2 star:		(0)
1 star:		(4)

 

 

This book is not an introductory text. Anyone interested in a particular topic should consult the preface of the text to find out what it is about. The negative reviewers were not fair to the authors on that score. Had they read the preface they would have found out (1) how the authors define data mining, (2) that they see it as a subject with an important mix of statistical methodology and computer science and (3) that it is intended as an advanced undergraduate or first year graduate text on the topic.
They also provide a very well organized structure for the text that is well described in the preface. It consists of three parts. Chapter 1 is an essential introduction that is informative to everyone. Chapters 2 through 4 go through basic statistical ideas that statisticians would be very familiar with and others could view as a refresher. The authors have experience teaching this course to engineering and science majors and have found that many of these students unfortunately do not have the prerequisite statistical inference ideas and need this material covered in the course.

Chapters 5 through 8 cover the components of data mining algorithms and the remaining chapters deal with the details of the tasks and algorithms.

The book features a further reading section at the end of each chapter that provides a very nice guide to the useful and most significant relevant literature. The author's have done a very good job at this. One mistake I found was a reference to Miller (1980). I think this was intended to be a reference to the seocnd edition fo Rupert Miller's text "Simultaneous Statistical Inference" which was published in 1981 by Springer-Verlag but the full citation is missing from the list of references in the back of the book.

This book deserves 5 stars because it does what it intends to do. It presents the field of data mining in a clear way covering topics on classfication and kernel methods expertly. David Hand has published a great deal on these techniques including many fine books.

Mannila and Smyth bring to the text the computer science perspective. There is much useful material on optimization methods and computational complexity.

Statistical modeling and issues of the "curse of dimensionality" and the "overfitting problem" are key issues that this text emphasizes and expertly addresses.

The only thing the text misses is details on specific algorithms. But I do not grade them down for that because it was not their intention. They emphasize methodology and issues and that is the most critical thing a practitioner needs to know first before embarking on his own attack at mining data.

The text does provide most of the current important methods. Although Vapnik's work is mentioned and his two books are referenced there is very little discussion of support vector machines and the use of Vapnik-Chervonenkis classes and dimension in data mining. The new book by Hastie, Tibshirani and Friedman goes into much greater detail on specific algorithms include some only briefly discussed in this text (e.g. support vector machines). The support vector approach is also nicely treated in "Learning with Kernels" by Scholkopf and Smola.

I highly recommend this book for anyone interested in data mining. It is a great reference source and an eloquent text to remind you of the pitfalls of thoughtless mining or "data-dredging". It also has many nice practical examples and some interesting success stories on the application of data mining to specific problems.

I am a professional data miner (20 yrs. experience) and data mining can be a treacherous business compared to conventional statistical analysis. There are many software packages that offer the novice a seemingly plethora of "information-extracting" tools. There is a tendency in the field to regard one or another of these as the final and eternal answer to a particular objective. This is the best guide so far in assisting the novice data miner in avoiding dumb mistakes and selecting the strongest analytical tool suited to data structure and objectives.

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.

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.

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.

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.

I'd been struggling with the seemingly infinite ways to approach data mining and this book cleared it all up for me. It is absolutely full of information and is a great base reference. It does not contain complete algorithms or step by step instructions (you can get those anywhere these days) but instead is a comprehensive survey of all the best known methods for data mining. I really like how the authors combined classical mining techniques with more modern ones (ex: Bayesian Networks). Other books try to stay in one camp or the other, all while denying that they use very similar sub-components.

This book is well worth it. I promise you will find more information than you could possibly retain.

It's not that this is a bad book, but you have to make sure you are right audience. The book offers very high-level overviews on various techniques of data mining, but it is almost impossible to learn how to really implement them. Since there are no exercises after each chapter you probably already know who the target audience of the book are.

Very good book for a general overview of data mining algorithms. Covers a wide variety of DM approaches.. however lacks concrete examples to clear concepts thoroughly. I especially liked Chapter-5 which gives a general framework to look at any DM algorithm. This clears confusion created by so many diverse algorithms with overlapping concepts and applications.

"Principles of Data Mining" was my first book on the subject, and although I haven't read it all, I can state that this book has done its job in explaining the fundamentals of the topic. It has a very well written recap of statistics and probability and is consistent throughout the chapters in terms of notation, which is important for such texts. Although my primary interest in this book was the EM algorithm (the coverage of which could have been better and longer), the other chapters that I read were fairly well written. I might be however a bit biased in my judgement, because one of the authors' office is 10 metres from mine :)

Bottom line: a good book, if you're interested in the subject. It's also not too expensive, considering other titles.

I bought this book because I wanted a relatively high level (not too high level, but high level enough to give me a good foundation in the theory and issues) to data mining. My goal was to first understand the theory and principles of data mining before getting into the technological and application specifics (e.g., how to use software such as Dataminer or R or Weka or SPSS Clementine etc.).

This book has met my goals. Most chapters include abstract math/statistics that may be a little challenging for people who do not have a recent high level undergraduate statistics background. Actually I enjoyed the math/stats, and did not worry about going too deep into those portions. Trust me, the abstract concepts are not easy to grasp beyond a certain point, but they are EXTREMELY valuable. I am really glad that I was challeged. If you want another perspective or intro to data mining you may want to read some of the lecture notes of the "Machine Learning" course from MIT's online courseware - the courses are available for free on MIT's online courseware site. The lecture notes are even more abstract - they will make you appreciate this book.

I highly recommend that anyone who wants to get an intro to data mining should first read this book. After reading this book the reader can read a book that explains a specific data mining software package such as "Intro to R" or "Data Mining: Practical Machine Learning Tools & Techniques" (by Witten and Frank, good if you want to learn Weka).