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Synopsis
Introduction.- Data Sets and Proper Statistical Analysis of Data Mining Techniques.- Data Preparation Basic Models.- Dealing with Missing Values.- Dealing with Noisy Data.- Data Reduction.- Feature Selection.- Instance Selection.- Discretization.- A Data Mining Software Package Including Data Preparation and Reduction: KEEL.
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Date Preprocessing in Data Mining
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Springer, 2015
ISBN 10 : 331910246X
ISBN 13 : 9783319102467
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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 réf. du vendeur 015047
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Data Preprocessing in Data Mining (Intelligent Systems Reference Library, 72)
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Data Preprocessing in Data Mining
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Springer International Publishing, 2014
ISBN 10 : 331910246X
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Gebunden. 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 réf. du vendeur 4498703
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Data Preprocessing in Data Mining
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Data Preprocessing in Data Mining
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Data Preprocessing in Data Mining
Edité par
Springer International Publishing, Springer Nature Switzerland, 2014
ISBN 10 : 331910246X
ISBN 13 : 9783319102467
Neuf
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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 réf. du vendeur 9783319102467
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Data Preprocessing in Data Mining
ISBN 10 : 331910246X
ISBN 13 : 9783319102467
Neuf
Couverture rigide
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 réf. du vendeur 9783319102467
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Data Preprocessing in Data Mining
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Springer, 2014
ISBN 10 : 331910246X
ISBN 13 : 9783319102467
Ancien ou d'occasion
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Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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Etat : As New. Unread book in perfect condition. N° de réf. du vendeur 21689995
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Data Preprocessing in Data Mining (Intelligent Systems Reference Library, 72)
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Springer, 2014
ISBN 10 : 331910246X
ISBN 13 : 9783319102467
Ancien ou d'occasion
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Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
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Hardcover. Etat : Like New. Like New. book. N° de réf. du vendeur ERICA773331910246X6
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Data Preprocessing in Data Mining
ISBN 10 : 331910246X
ISBN 13 : 9783319102467
Neuf
Couverture rigide
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
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Buch. Etat : Neu. 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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 336 pp. Englisch. N° de réf. du vendeur 9783319102467
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