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Principles of Data Mining, Second Edition (Undergraduate Topics in Computer Science) - Couverture souple
Synopsis
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
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Présentation de l'éditeur
Data Mining, the automatic extraction of implicit and potentially useful information from data, is increasingly used in commercial, scientific and other application areas. Principles of Data Mining explains and explores the principal techniques of Data Mining: for classification, association rule mining and clustering. Each topic is clearly explained and illustrated by detailed worked examples, with a focus on algorithms rather than mathematical formalism. It is written for readers without a strong background in mathematics or statistics, and any formulae used are explained in detail. This second edition has been expanded to include additional chapters on using frequent pattern trees for Association Rule Mining, comparing classifiers, ensemble classification and dealing with very large volumes of data. Principles of Data Mining aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Suitable as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science.
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- ÉditeurSpringer London Ltd
- Date d'édition2013
- ISBN 10 1447148835
- ISBN 13 9781447148838
- ReliureBroché
- Langueanglais
- Numéro d'édition2
- Nombre de pages440
- Coordonnées du fabricantnon disponible
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Principles of Data Mining
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Springer, 2013
ISBN 10 : 1447148835
ISBN 13 : 9781447148838
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Principles of Data Mining, Second Edition (Undergraduate Topics in Computer Science)
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Springer (edition 2nd ed. 2013), 2013
ISBN 10 : 1447148835
ISBN 13 : 9781447148838
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Principles of Data Mining, Second Edition (Undergraduate Topics in Computer Science)
Edité par
Springer, 2013
ISBN 10 : 1447148835
ISBN 13 : 9781447148838
Ancien ou d'occasion
Couverture souple
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