Hardcover. Etat : Fine. 1st Edition. A detailed discussion of data mining with particular focus on the relationship between data acquisition from the source and the data mining process. 320 pages. No sign of previous use. N� de r�f. du vendeur 015047

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Data Preprocessing in Data Mining

Salvador Garc�a|Juli�n Luengo|Francisco Herrera

Edit� par Springer International Publishing, 2014

ISBN 10 : 331910246X ISBN 13 : 9783319102467

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Covers the set of techniques under the umbrella of data preprocessing in data mining and machine learningA comprehensive book devoted completely to preprocessing in data miningWritten by experts in the fieldData Preprocessing . N� de r�f. du vendeur 4498703

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Data Preprocessing in Data Mining (Intelligent Systems Reference Library, 72)

Garc�a

Edit� par Springer, 2014

ISBN 10 : 331910246X ISBN 13 : 9783319102467

Neuf Couverture rigide

Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni

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Etat : New. In. N� de r�f. du vendeur ria9783319102467_new

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Data Preprocessing in Data Mining

Salvador Garc�a

Edit� par Springer International Publishing, Springer Nature Switzerland, 2014

ISBN 10 : 331910246X ISBN 13 : 9783319102467

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering. N� de r�f. du vendeur 9783319102467

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Data Preprocessing in Data Mining

Salvador Garc�a

Edit� par Springer International Publishing, Springer Nature Switzerland Sep 2014, 2014

ISBN 10 : 331910246X ISBN 13 : 9783319102467

Neuf Couverture rigide

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data.This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering. 336 pp. Englisch. N� de r�f. du vendeur 9783319102467

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