- Series: Adaptive Computation and Machine Learning Series
- Hardcover: 578 pages
- Publisher: A Bradford Book; 1st US Edition 1st Printing edition (August 1, 2001)
- Language: English
- ISBN-10: 026208290X
- ISBN-13: 978-0262082907
- Product Dimensions: 8 x 1.2 x 9 inches
- Shipping Weight: 2.5 pounds (View shipping rates and policies)
- Average Customer Review: 13 customer reviews
- Amazon Best Sellers Rank: #840,816 in Books (See Top 100 in Books)
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Principles of Data Mining (Adaptive Computation and Machine Learning) 1st US Edition 1st Printing Edition
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About the Author
David Hand is Professor of Statistics, Department of Mathematics, Imperial College, London.
Top customer reviews
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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).
I hope the authors are planning a new edition to match the changes in technology.
This book is well worth it. I promise you will find more information than you could possibly retain.
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.
It does a poor job explaining the theory.
It does a poor job giving practical "hands on" advice.
SAVE YOUR MONEY, AVOID THIS BOOK !!!