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Principles of Data Mining (Adaptive Computation and Machine Learning Series) Hardcover – 10 Oct 2001

4.3 out of 5 stars 3 customer reviews

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Product details

  • Hardcover: 578 pages
  • Publisher: MIT Press (10 Oct. 2001)
  • Language: English
  • ISBN-10: 026208290X
  • ISBN-13: 978-0262082907
  • Product Dimensions: 20.3 x 2.5 x 22.9 cm
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Bestsellers Rank: 225,624 in Books (See Top 100 in Books)

Product Description

About the Author

David J. Hand is an emeritus professor of mathematics and a senior research investigator at Imperial College London. He is the former president of the Royal Statistical Society and the chief scientific adviser to Winton Capital Management, one of Europe s most successful algorithmic-trading hedge funds. He is the author of several books, including The Information Generation: How Data Rules Our World, Statistics: A Very Short Introduction, and The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day, has published more than three hundred scientific papers. Hand lives in London, England.


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Format: Hardcover
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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Format: Hardcover
It is absolutely a wonderful book for beginners in the filed of data mining. I recommend this book for undergraduates and all those who just start reading about data mining topics.
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Format: Hardcover Verified Purchase
not much to comment on really, 94% of IT is dead, meaning 94% of MD's have been kicked out for being fake.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 3.6 out of 5 stars 18 reviews
40 of 40 people found the following review helpful
5.0 out of 5 stars finally a good statistical and computer science perspective on data mining 23 Jan. 2008
By Michael R. Chernick - Published on Amazon.com
Format: Hardcover
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.
38 of 39 people found the following review helpful
5.0 out of 5 stars A wonderful book but not a cookbook 10 Nov. 2003
By Robert Ehrlich - Published on Amazon.com
Format: Hardcover
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.
20 of 22 people found the following review helpful
5.0 out of 5 stars Excellent introductory text on data mining 29 April 2003
By A Customer - Published on Amazon.com
Format: Hardcover
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 people found the following review helpful
5.0 out of 5 stars Excellent Text! 16 Jan. 2002
By A Customer - Published on Amazon.com
Format: Hardcover
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.
9 of 10 people found the following review helpful
5.0 out of 5 stars A welcome addition to data mining 18 Dec. 2001
By A Customer - Published on Amazon.com
Format: Hardcover
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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