Selected Product:
| Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Paperback
Edition: 2
Author: Ian H. Witten, Eibe Frank
Publisher: Morgan Kaufmann
Release Date: 2005-06-22
ISBN-10: 0120884070
ISBN-13: 9780120884070
List Price: $65.95
Average Customer Rating:
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Summaries and Customer Reviews are supplied by Amazon.com
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface
very useful academically, but not industry focused
| Customer Rating: | It is a very clear and easy reading 'machine learning' book to read, but its not a 'data mining' book. Everyone in the industry agrees that over 80% of your time and effort is in the data preparation, yet this book has virtually no mention of data transformations or data preparation.
It is a good book that describes how algorithsm works, their pros and cons. Very useful for new starters and academics. It won't help a industry practitioner though.
Page 360 onwards to 500 are dedicated to using a freeware data mining tool named Weka.
The book was worth the buy, but I had hoped for more.
- Tim |
Not particularly useful
| Customer Rating: | | The material is very superficially laid out and for a book with the word "Practical" in the sub-title it contains almost no practical examples of data mining. |
Thorough, well-written, and crystal-clear explanations.
| Customer Rating: | Highly recommend this book for a practical introduction to the theory and applications of Machine Learning. Great book if you are looking to ACTUALLY implement some machine learning systems, prefer to learn via diagrams, a "how-stuff-works"-style explanation, and skip much of the equations and heavy math that fills similar books.
Obviously, this book is a perfect companion to the Weka machine toolbox, which is quickly becoming a standard, invaluable research toolbox for many. |
A little too wordy for my tastes, but good
| Customer Rating: | | This book was pretty good. I have to admit that for the first hundred or so pages, I was feeling very impatient. All of that information could have been conveyed in about 25 pages, and been much easier to read. But there are some very good examples in here, and it is worth reading. If you are looking for something more technical, try "Pattern Recognition and Machine Learning", by Christopher M. Bishop or "The Elements of Statistical Learning" by Hastie, Tibshirani, and Friedman. |
Superficial
| Customer Rating: | This book reminds me of the programming books by Deitel&Deitel. It's wordy and superficial, making lots of people feel like they understand the subject. Unfortunately, it takes *much* more than what's in this book to really understand Data Mining. Compare this book to the book by Hastie, Friedman and Tibshiranie, which really goes into the statistics involved in Data Mining.
There is no magic: real Data Mining needs lots of Statistics. You can learn to use Weka, but in order to do real work you'll need to understand what goes behind its nice user interface, and I think this book is not enough. |
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