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Data Mining Practical Machine Learning Tools and Techniques

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ISBN-10: 0123748569

ISBN-13: 9780123748560

Edition: 3rd 2011

Authors: Ian H. Witten, Eibe Frank, Geoffrey Holmes, Mark A. Hall

List price: $69.95
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Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques 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 today as well as methods at the leading edge of contemporary research. Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download.…    
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Book details

List price: $69.95
Edition: 3rd
Copyright year: 2011
Publisher: Elsevier Science & Technology Books
Publication date: 1/6/2011
Binding: Paperback
Pages: 664
Size: 7.50" wide x 9.25" long x 1.25" tall
Weight: 2.442
Language: English

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer…    

Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.

Machine Learning Tools and Techniques
What's It All About?
Input: Concepts, Instances, Attributes
Output: Knowledge Representation: Algorithms
The Basic Methods
Credibility: Evaluating What's Been Learned
Implementations: Real Machine Learning Schemes
Data Transformation
Ensemble Learning
Massive Data Sets
Practical Data Mining
The Weka Machine Learning Workbench
Intro to Weka
The Explorer
The Knowledge Flow Interface
The Experimenter
The Command-Line Interface
Embedded Machine Learning
Writing New Learning Schemes