- Paperback: 664 pages
- Publisher: Morgan Kaufmann; 3 edition (3 Feb 2011)
- Language English
- ISBN-10: 0123748569
- ISBN-13: 978-0123748560
- Product Dimensions: 23.1 x 18.8 x 3.8 cm
- Average Customer Review: 5.0 out of 5 stars See all reviews (1 customer review)
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Amazon Bestsellers Rank:
83,988 in Books (See Top 100 in Books)
- #6 in Books > Computers & Internet > Computer Science > Algorithms > Machine Learning
- #7 in Books > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning
- #12 in Books > Computers & Internet > Computer Science > Databases > Data Storage & Management > Data Mining
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Product Description
Review
"The authors provide enough theory to enable practical application, and it is this practical focus that separates this book from most, if not all, other books on this subject."- Dorian Pyle, Director of Modeling at Numetrics and an internationally known author of Data Preparation for Data Mining (Morgan Kaufmann, 1999) and Business Modeling for Data Mining (Morgan Kaufmann, 2003)
"This book would be a strong contender for a technical data mining course. It is one of the best of its kind."- Herb Edelstein, Principal, Data Mining Consultant, Two Crows Consulting.
"It is certainly one of my favorite data mining books in my library"- Tom Breur, Principal, XLNT Consulting, Tilburg, The Netherlands
--Tom Breur, Principal, XLNT Consulting, Tilburg, The NetherlandsProduct Description
Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research.
*Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
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Customer Reviews
Most Helpful Customer Reviews
1 of 1 people found the following review helpful
Format:Paperback
I have been using Witten's book since its first edition for my course on Data Mining. The first edition was probably too limited (few topics, not discussed in depth). The second and this third edition have improved the original project much more. My students seem to prefer this more than Han's textbook.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
18 reviews
43 of 43 people found the following review helpful
Worthwhile Update to an Excellent Text
6 Mar 2011
Format:Paperback|Amazon Vine™ Review
(What's this?)
Context for this review: I am a data miner with 20 years experience, and own the first edition of this book.
Good:
- Accessible writing style
- Broad coverage of algorithms and data mining issues, with an eye toward practical issues
- Needless technical trivia (derivations and the like) are avoided
- Algorithms are completely spelled out: A competent programmer should be able to turn these descriptions into functioning code.
- Third edition makes meaningful improvements on previous editions
Bad(ish):
- Approximately one-third of this book is now devoted to the WEKA data mining software. I have nothing against WEKA, and it is a good choice for a text such as this, since WEKA is free. In my opinion, though, this coverage consumes too many pages of this book.
- Data mining draws from a number of fields with separate roots (statistics, machine learning, pattern recognition, engineering, etc.), and many techniques go by multiple names. As with many other data mining books, this one does not always point out the aliases by which data mining methods are known.
The bottom line: This is still the best data mining text on the market.
Good:
- Accessible writing style
- Broad coverage of algorithms and data mining issues, with an eye toward practical issues
- Needless technical trivia (derivations and the like) are avoided
- Algorithms are completely spelled out: A competent programmer should be able to turn these descriptions into functioning code.
- Third edition makes meaningful improvements on previous editions
Bad(ish):
- Approximately one-third of this book is now devoted to the WEKA data mining software. I have nothing against WEKA, and it is a good choice for a text such as this, since WEKA is free. In my opinion, though, this coverage consumes too many pages of this book.
- Data mining draws from a number of fields with separate roots (statistics, machine learning, pattern recognition, engineering, etc.), and many techniques go by multiple names. As with many other data mining books, this one does not always point out the aliases by which data mining methods are known.
The bottom line: This is still the best data mining text on the market.
17 of 17 people found the following review helpful
Applying Machine Learning to Data Mining problems
1 April 2011
Format:Paperback|Amazon Vine™ Review
(What's this?)
The subtitle of the book should really be emphasized more: Practical Machine Learning Tools and Techniques. This isn't a book about adhoc SQL queries and database statistics, it is about tools to discover relationships you didn't know you were looking for. Much of the book shows how to handle knowledge formation and representation, statistical modeling and projections. The one critique I have in regard is that much of the algorithm breakdowns are done in prose rather than true pseudocode.
I would like to echo other reviews that point out the text focuses on WEKA, and the authors indicate this is by intent. Though they do give much generic information, at some point you have to pick a horse to hitch your carriage to, and an established open-source project in Java is probably most widely accessible. Their coverage of WEKA claims 50% more features than the 2nd ed. and indeed it consumes half the book. I feel this is a good thing, as it lends great practicality to the book, allowing you to dig right in and get something actually done.
There are some additions to the 3rd ed. that modernize the book a bit. Showing how data can be reidentified (and the ethical implications) is pertinent to today's HIPAA-regulated medical environments. They also touch on web and ubiquitous mining, reflecting our growing foray into non-traditional cloud sources of information.
I would like to echo other reviews that point out the text focuses on WEKA, and the authors indicate this is by intent. Though they do give much generic information, at some point you have to pick a horse to hitch your carriage to, and an established open-source project in Java is probably most widely accessible. Their coverage of WEKA claims 50% more features than the 2nd ed. and indeed it consumes half the book. I feel this is a good thing, as it lends great practicality to the book, allowing you to dig right in and get something actually done.
There are some additions to the 3rd ed. that modernize the book a bit. Showing how data can be reidentified (and the ethical implications) is pertinent to today's HIPAA-regulated medical environments. They also touch on web and ubiquitous mining, reflecting our growing foray into non-traditional cloud sources of information.
15 of 16 people found the following review helpful
Mixed Opinion
28 April 2011
Format:Paperback|Amazon Vine™ Review
(What's this?)
Fantastic book if you need to use WEKA; probably the best recommendation available.
If, however, you're not going to be using WEKA then the book is still valuable, but I challenge the true 'practicality' of it. The content is thorough but perhaps more academically oriented than as industry focused as I would have liked. The author keeps it very accessible, particularly as far as mathematics and statistics go. While this might make the book a little more long winded - in my view it makes it a far easier to get into the groove and allows you to read it like a book.
* Highly recommended for WEKA users
* For others users I suggest you look through to see if it will really be helpful before plunking down the cash
If, however, you're not going to be using WEKA then the book is still valuable, but I challenge the true 'practicality' of it. The content is thorough but perhaps more academically oriented than as industry focused as I would have liked. The author keeps it very accessible, particularly as far as mathematics and statistics go. While this might make the book a little more long winded - in my view it makes it a far easier to get into the groove and allows you to read it like a book.
* Highly recommended for WEKA users
* For others users I suggest you look through to see if it will really be helpful before plunking down the cash
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