Selected Product:
| Principles of Data Mining (Adaptive Computation and Machine Learning)
Hardcover
Author: David J. Hand, Heikki Mannila, Padhraic Smyth
Publisher: The MIT Press
Release Date: 2001-08-01
ISBN-10: 026208290X
ISBN-13: 9780262082907
List Price: $65.00
Average Customer Rating:
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Summaries and Customer Reviews are supplied by Amazon.com
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.
finally a good statistical and computer science perspective on data mining
| Customer Rating: | 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.
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make sure you are right audience
| Customer Rating: | | 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. |
It shows me many examples
| Customer Rating: | | Even if it is bad as all the gentlemen said, I think at least it gives me many examples which are not mentioned in other books before. |
Very, Bad Book !
| Customer Rating: | I was very disappointed in this book. There are so many other books in the field of Data Mining that are so much better. This one has very little to offer.
It does a poor job explaining the theory.
It does a poor job giving practical "hands on" advice.
SAVE YOUR MONEY, AVOID THIS BOOK !!! |
Good book for overall breadth of alogrithms..
| Customer Rating: | | 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. |
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