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

by Ian H. Witten (Author) "What is meant by ""structural patterns""? ..." (more)
4.5 out of 5 stars  See all reviews (2 customer reviews)
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  • This item: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) by Ian H. Witten

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Product Description

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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


Product Description

This book 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. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.


Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.



* Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques.
* Covers performance improvement techniques, including input preprocessing and combining output from different methods.
* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.
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Inside This Book (Learn More)
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"What is meant by ""structural patterns""?" Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems)
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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems) 4.5 out of 5 stars (2)
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4.5 out of 5 stars (2 customer reviews)
 
 
 
 
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2 of 2 people found the following review helpful:
5.0 out of 5 stars Simple, complete, useful, 16 April 2003
This book is able to explain the "great design" and the details too.
All chapters are both clear and intuitive.
You need only a standard knowledge of statistics and math: the WEKA software (that you can download from the author's site) is very useful for a professional approach too.
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4.0 out of 5 stars This first edition is a worthy introduction to Data Mining, 21 May 2006
By D. Berry "Alex Berry" (United Kingdom) - See all my reviews
(REAL NAME)   
Those studying the progress of data mining will quickly realise that the scientifically and academically charged subject has evolved rather slowly in the commercial world. This book, for when it was first written, did an excellent job of presenting the key concepts and working through them in an effective way.

Particularly attractive to those with an application development interest. The introduction to the WEKA environment offers a great set of resources difficult to ignore. Highly recommended for practitioners.

Cleary, data mining is still developing. With certain applications, the algorithms and parameters are being pushed continually further away from end-users and hidden with varying levels of automation. For others, perhaps more recent techniques, less is known and therefore a greater level of control is required to allow for adequate experimentation.

In any case, this book should well worth digesting, although it has been superceded by the second edition, Data Mining, Practical Machine Learning Tools & Techniques, 2005, which, on the whole, does the job even better - so I'd go for that unless the first edition is heavily discounted.
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