Customer Reviews

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

5 star:		(11)
4 star:		(2)
3 star:		(1)
2 star:		(1)
1 star:		(2)

 

 

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.

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.

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.

In fall 2000, I taught a master's level course in ML to about 25students at New York University. Fortunately both for me and mystudents, I was able to use and assign excellent recent textbooks inthe area: "Machine Learning" by Tom Mitchell and "DataMining: Practical Machine Learning Tools and Techniques with JavaImplementations" by Ian H. Witten and Eibe Frank. I recommendboth books enthusiastically.

A student who has mastered Mitchellhas a solid grasp of the basic element of nearly every method ofmachine learning currently in use, and of almost every aspect of MLresearch. A student who has mastered Witten/Frank has a deepknowledge of the major ML techniques, and a strong sense of theopportunities and pitfalls to be encounted when these techniques areput into practice.

A complete review of both books, to appear inArtificial Intelligence Journal, can be found at...

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.

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.

This is a nice book which explains in easy-to-understand English some of the concepts that you might read elsewhere in papers, etc. I can't comment on the usefulness of this book in a non-research or "totally business" type of environment, but for the industry/academic hybrid researcher, this book is recommended. I wish I could add it to my amazon Data Mining list, but lists can't be edited at this time.

I've been working with "big name software" for some years, but when I joined the institution I work now and no tools where available I begun my quest for an open source tool that could help me build statistical models applied to real business problems.

As a result of this quest I found the WEKA data mining software on the Internet (you can find it on www.cs.waikato.ac.nz/~ml/weka/) and that nice piece of software leaded me to this book.

This book is EXCELLENT and I am giving 5 *five* stars to it as it helped me understanding the whole process of datamining: from loading the data to building the model.

I've read some reviews and I think some of them are not fair (particularly one that says that this book have "just words with no relation or sense at all").. THIS BOOK IS REALLY WELL WRITTEN but you have to read it slowly: As when you study something.

Buy this book (*don't forget to download the software*) and I am totally sure that you will be producing and using models in a week.

Can't imagine that some weeks ago

Cheers,

Witten's book, combined with the accompanying open source package, Weka, provides a great overview of data mining principles and practice from a machine learning perspective. One could hardly do better than to own this book and "The Elements of Statistical Learning; Data Mining, Inference, and Prediction" by Hastie, Tibshirani, and Friedman which covers complimentary material from the statistician's perspective. Witten does an amazing job of providing a comprehensive overview of the field while still providing some depth re. the algorithms; after reading the book I didn't feel like I'd read yet another large volume of empty claims about the power of information technology to make me rich and famous. In fact, with the book by my side, in a relatively short time I was able to use Weka to pry some useful information from one of my medical imaging data sets (maybe even enough to serve as preliminary data for a grant application). It seems to me that with an understanding of the material in this book and the one by Hastie et. al. one could embark on serious data mining projects.

This book is actual a textbook for a data analysis course. We use it because the flow of the chapters is almost the same as the flow of the course material. Unfortunately, it is not as useful as expected if you are in the field. It is not in depth for the materials that the authors wanted to cover due to the fact that this is not a book for just programming or just statistics. If you have a strong background on machine language or a strong background on data analysis, you may not find it useful for you career. This book is for those who have limited knowledge on both programming and statistics, but not for professionals.