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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) Paperback – 20 Oct 1999


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Product details

  • Paperback: 371 pages
  • Publisher: Morgan Kaufmann Publishers In (20 Oct. 1999)
  • Language: English
  • ISBN-10: 1558605525
  • ISBN-13: 978-1558605527
  • Product Dimensions: 23.2 x 18.5 x 2.1 cm
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
  • Amazon Bestsellers Rank: 70,961 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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"This is a milestone in the synthesis of data mining, data analysis, information theory, and machine learning." - Jim Gray, Microsoft Research

About the Author

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann. Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.

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2 of 2 people found the following review helpful By Paolo Cavicchini on 16 April 2003
Format: Paperback
This book is able to explain the "great design" and the details too.
All chapters are both clear and intuitive.
You need only a standard knowledge of statistics and math: the WEKA software (that you can download from the author's site) is very useful for a professional approach too.
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Format: Paperback
Those studying the progress of data mining will quickly realise that the scientifically and academically charged subject has evolved rather slowly in the commercial world. This book, for when it was first written, did an excellent job of presenting the key concepts and working through them in an effective way.

Particularly attractive to those with an application development interest. The introduction to the WEKA environment offers a great set of resources difficult to ignore. Highly recommended for practitioners.

Cleary, data mining is still developing. With certain applications, the algorithms and parameters are being pushed continually further away from end-users and hidden with varying levels of automation. For others, perhaps more recent techniques, less is known and therefore a greater level of control is required to allow for adequate experimentation.

In any case, this book should well worth digesting, although it has been superceded by the second edition, Data Mining, Practical Machine Learning Tools & Techniques, 2005, which, on the whole, does the job even better - so I'd go for that unless the first edition is heavily discounted.
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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 16 reviews
35 of 36 people found the following review helpful
Excellent introduction to data mining algorithms 7 Feb. 2000
By Dean - Published on Amazon.com
Format: Paperback
Witten and Frank have generated a book that is readable without eliminating all technical (yes, even mathematical!) descriptions of the key data mining algorithms. And they are up-to-date, including support vector machines and boosting. There are sufficient examples of the techniques to provide readers with a good feel for what each technique can accomplish. For example, how many books can provide a readable explanation of support vector machines?
There are some quibbles, such as not including any discussion of neural networks (noted in Ch. 1 with another reference)--I believe it deserves some attention because of its widespread use. Additionally, future editions should include a least a brief summary of data preprocessing, input selection, feature creation, etc. But these are quibbles.
The Java portion of the book is not of as much interest to me, but for those wishing to implement the algorithms, it provides a nice blueprint (from the code I looked at).
For what they have undertaken, they have performed admirably, and I would highly recommend this book.
26 of 27 people found the following review helpful
Excellent data mining textbook 3 Dec. 1999
By Stan Matwin - Published on Amazon.com
Format: Paperback
Broad coverage, including hot new topics: SVM, boosting and bagging, modern evaluation methods (ROC and lift curves). Well grounded in practical data mining applications, talks about DM issues outside model building, which are rarely discussed: feature engineering, data cleaning, etc. Clear and well written: illustrative examples help the presentation a lot. Describes in detail decision trees and rule learners, instance-based learning, and numerical prediction. Accompanied by the WEKA system, implementing in Java many of the methods discussed in the book, and available for download for free. An excellent hands-on textbook for an applied Machine Learening/DM class, or recommended reading for ayone who wants to understand DM. Good next step for those that have whetted their appetite with Berry and Linof's book.
26 of 27 people found the following review helpful
You HAVE to read this book! 28 Jan. 2000
By Bostjan Brumen - Published on Amazon.com
Format: Paperback
This book is THE best book I have read about data mining. And I have read most of them (see ISBNs: 0070057796, 0471253847, 0262560976, 0201403803, 0471179809, 013743980, 0137564120, 1558605290, 1558604030). It is fresh, clear, well balanced. If your native language is not English, then you should definetly read THIS book first.
The feature that is the most important for me is "just enough statistics". That is, you can understand the processes & descriptions even if you have not wasted your life and youth studying statistics; what is needed of it to understand is given shortly and very well. Many other books are too deep or too shallow (like Berry's, which is a good introduction, but nothing more than that).
If the rating was scaled 1-6 stars, I'd give this book a 10.
13 of 13 people found the following review helpful
Data mining technology power on 400 pages. 28 Feb. 2002
By Stefan Groschupf - Published on Amazon.com
Format: Paperback
It's difficult to get interesting
literature related to this theme.
On the one hand there are some books written for managers, on the other hand there are some pretty mathematical books for academics. But this book is the best mix. You get an introduction to data mining and learn step by step from the basics up to the hard algorithm stuff with nice examples.
There is a clear theme structure, and the deep technical sections are marked, so you can read what you are most interested in. The book describes not only one algorithm, but a lot of them and discusses plusses and minuses. Where it's necessary it uses simple diagrams to illustrate something, not so much that it looks like they want to fill the pages, like in other books. Best of all, the algorithms are implemented as an
open source java software named "weka". This is my state of the art data mining tool.
You can see the algorithms working and use the implementations for your ideas (like me). If you are hungry to learn more
about one or the other thing, the book provides a literature list.
For me this book was one of the best books in the last years, because it provides the best mix and gives you a fast but deep view in this theme.
12 of 12 people found the following review helpful
Promotes a deep understanding of the topic 25 Nov. 1999
By Brent Martin - Published on Amazon.com
Format: Paperback
This book is excellent for anyone entering the fields of data mining or machine learning. The material is organised into functions rather than techniques, which promotes a deeper understanding of why different approaches work, when to use them, and how they can be combined to maximise results.
For those already conversant in machine learning, it contains a wealth of practical techniques for improving and analysing results. I expect to use it often in the course of my research.
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