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Data preprocessing in data mining

Garcia, Salvador.

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Data preprocessing in data mining

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data preprocessing in data mining/ by Salvador Garcia, Julian Luengo, Francisco Herrera.
Author:	Garcia, Salvador.
other author:	Luengo, Julian.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xv, 320 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Data mining. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-10247-4
ISBN:	9783319102474 (electronic bk.)

Data preprocessing in data mining
Garcia, Salvador.

Data preprocessing in data mining[electronic resource] /by Salvador Garcia, Julian Luengo, Francisco Herrera. - Cham :Springer International Publishing :2015. - xv, 320 p. :ill., digital ;24 cm. - Intelligent systems reference library,v.721868-4394 ;. - Intelligent systems reference library ;v.24..

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.

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.

ISBN: 9783319102474 (electronic bk.)

Standard No.: 10.1007/978-3-319-10247-4doiSubjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.3

Data preprocessing in data mining
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245 1 0 $a Data preprocessing in data mining $h [electronic resource] / $c by Salvador Garcia, Julian Luengo, Francisco Herrera.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xv, 320 p. : $b ill., digital ; $c 24 cm.
490 1 $a Intelligent systems reference library, $x 1868-4394 ; $v v.72
505 0 $a 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.
520 $a 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.
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