Data Preprocessing in Data Mining (Record no. 56587)

000 -LEADER
fixed length control field 03596nam a22005175i 4500
001 - CONTROL NUMBER
control field 978-3-319-10247-4
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421112040.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 140830s2015 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319102474
-- 978-3-319-10247-4
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Garc�ia, Salvador.
245 10 - TITLE STATEMENT
Title Data Preprocessing in Data Mining
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 320 p. 41 illus.
490 1# - SERIES STATEMENT
Series statement Intelligent Systems Reference Library,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 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 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Luengo, Juli�an.
700 1# - AUTHOR 2
Author 2 Herrera, Francisco.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-10247-4
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2015.
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data mining.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image processing.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Image Processing and Computer Vision.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Data Mining and Knowledge Discovery.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1868-4394 ;
912 ## -
-- ZDB-2-ENG

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