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Data mining : practical machine learning tools and techniques / Ian H. Witten, Department of Computer Science, University of Waikato, Eibe Frank, Department of Computer Science, University of Waikato.

By: Witten, I. H. (Ian H.) [author.]
Contributor(s): Frank, Eibe [author.]
Material type: TextTextSeries: Morgan Kaufmann series in data management systemsPublisher: San Francisco, CA : Morgan Kaufman Publishers, 2006Edition: Second edition; Reprinted editionDescription: xxxi, 525 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 0120884070; 9788131200506; 8131200507Subject(s): Data mining | Informatics and Computer Science March2020Genre/Form: -- Reading book DDC classification: 006.312 Online resources: Publisher description | Table of contents only Summary: 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; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you'll find: 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
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Item type Current location Collection Call number Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library
Lower Floor
Baccah 006.312 WIT (Browse shelf) Available 000048781
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Includes bibliographical references and index.

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; comprehensiv inforation on neural networks; a new section on Bayesian networks; plus much more. Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you'll find: 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

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