Data Mining Practical Machine Learning Tools and Techniques

ISBN-10: 0123748569
ISBN-13: 9780123748560
Edition: 3rd 2011
List price: $69.95 Buy it from $13.48
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Description: 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  More...

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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: 1.144
Language: English

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. The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material. * Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques * Algorithmic methods at the heart of successful data mining?including tired and true methods 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 an updated, interactive interface.

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 typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.

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

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