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Record Nr. |
UNINA9910449698103321 |
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Autore |
Witten I. H (Ian H.) |
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Titolo |
Data mining [[electronic resource] ] : practical machine learning tools and techniques / / Ian H. Witten, Eibe Frank |
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Pubbl/distr/stampa |
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Amsterdam ; ; Boston, MA, : Morgan Kaufman, 2005 |
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ISBN |
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9786611008062 |
0-08-047702-X |
9781423722442 |
1-281-00806-0 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (xxxi, 524 p.) : ill |
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Collana |
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Morgan Kaufmann series in data management systems |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Data mining |
Database searching |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references (p. 485-503) and index. |
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Nota di contenuto |
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PART I: MACHINE LEARNING TOOLS AND TECHNIQUES; 1 What's it all about?; 2 Input: Concepts, instances, and attributes; 3 Output: Knowledge representation; 4 Algorithms: The basic methods; 5 Credibility: Evaluating what's been learned; 6 Implementations: Real machine learning schemes; 7 Transformations: Engineering the input and output; 8 Moving on: Extensions and applications; PART II: THE WEKA MACHINE LEARNING WORKBENCH; 9 Introduction to Weka; 10 The Explorer; 11 The Knowledge Flow Interface; 12 The Experimenter; 13 The Command-Line Interface; 14 Embedded machine learning; 15 Writing New Learning Schemes; References; Index; About the Authors. |
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Sommario/riassunto |
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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 |
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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; comprehensive information on neural networks; a new section on Bayesian networks; plus much more. 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 in a new, interactive interface. |
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