Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
||Author: Ian H. Witten, Eibe Frank|
List Price: $49.95
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Publisher: Morgan Kaufmann (11 October, 1999)
Sales Rank: 15,138
Average Customer Rating: 4 out of 5
Customer ReviewsRating: 5 out of 5
If you read machine learning then you should read it also
I have read machine learning writed by Tom M. Mitchell and also I have read Data Mining Concepts and Techniques writed by J. Han and M. Kamber. Both text books is very useful for someone who want to get concept of a modern data analysis approaches. But, however, to understant about that clearly, you should read this book also because the example and author's form writing is so good and nice, very easy to understant.
Rating: 1 out of 5
Poor writing, often delves on irritating jokes and unimportant topics (for instance I didn't buy this book to tell me about how cool javadoc is), fails to deliver complete mathematical background for the models, fails to give a good explanation on how to use Weka software.
Overall it's a big black hole that'll eat away a chunk of your time while providing a super low return in useful knowledge.
Can't they write a few separate chapters that provide all the information you need and teach you a few algorithms instead of trying to be an encyclopedia and be so shallow as they are?
Academic, hard to follow, often references other books for critical info, poorly organized. Skip it while it's not too late.
Rating: 5 out of 5
Data mining technology power on 400 pages.
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.
· Data Mining: Concepts and Techniques
· Principles of Data Mining (Adaptive Computation and Machine Learning)
· Mining the Web: Analysis of Hypertext and Semi Structured Data
· Machine Learning
· The Elements of Statistical Learning