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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
 
 
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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations [Paperback]

Ian H. Witten (Author), Eibe Frank (Author)
4.0 out of 5 stars  See all reviews (14 customer reviews)
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Data mining techniques are used to power intelligent software, both on and off the Internet. Data Mining: Practical Machine Learning Tools explains the magic behind information extraction in a book that succeeds at bringing the latest in computer science research to any IS manager or developer. In addition, this book provides an opportunity for the authors to showcase their powerful reusable Java class library for building custom data mining software.

This text is remarkable with its comprehensive review of recent research on machine learning, all told in a very approachable style. (While there is plenty of math in some sections, the authors' explanations are always clear.) The book tours the nature of machine learning and how it can be used to find predictive patterns in data comprehensible to managers and developers alike. And they use sample data (for such topics as weather, contact lens prescriptions, and flowers) to illustrate key concepts.

After setting out to explain the types of machine learning models (like decision trees and classification rules), the book surveys algorithms used to implement them, plus strategies for improving performance and the reliability of results. Later the book turns to the authors' downloadable Weka (rhymes with "Mecca") Java class library, which lets you experiment with data mining hands-on and gets you started with this technology in custom applications. Final sections look at the bright prospects for data mining and machine learning on the Internet (for example, in Web search engines).

Precise but never pedantic, this admirably clear title delivers a real-world perspective on advantages of data mining and machine learning. Besides a programming how-to, it can be read profitably by any manager or developer who wants to see what leading-edge machine learning techniques can do for their software. --Richard Dragan

Topics covered: Data mining and machine learning basics, sample datasets and applications for data mining, machine learning vs. statistics, the ethics of data mining, generalization, concepts, attributes, missing values, decision tables and trees, classification rules, association rules, exceptions, numeric prediction, clustering, algorithms and implementations in Java, inferring rules, statistical modeling, covering algorithms, linear models, support vector machines, instance-based learning, credibility, cross-validation, probability, costs (lift charts and ROC curves), selecting attributes, data cleansing, combining multiple models (bagging, boosting, and stacking), Weka (reusable Java classes for machine learning), customizing Weka, visualizing machine learning, working with massive datasets, text mining, and e-mail and the Internet.

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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, USA

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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Customer Reviews

14 Reviews
5 star:
 (9)
4 star:
 (1)
3 star:
 (1)
2 star:
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1 star:
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Average Customer Review
4.0 out of 5 stars (14 customer reviews)
 
 
 
 
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5.0 out of 5 stars Stop searching for datamining: You've found it., April 5 2004
By 
J. ALBARRACIN "Javier Valdivia" (Lima, Peru) - See all my reviews
(REAL NAME)   
This review is from: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Paperback)
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,

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3.0 out of 5 stars Try to cover many, but not depth enough., Jan 23 2004
By A Customer
This review is from: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Paperback)
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.
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5.0 out of 5 stars If you read machine learning then you should read it also, Jan 19 2004
By 
Norraseth Chantasut (NECTEC, 112 Thailand Science Park, Thailand) - See all my reviews
This review is from: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (Paperback)
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.
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