000			03272cam a22003615i 4500
001			21740290
003			OSt
005			20241030142257.0
006			m |o d |
007			cr |||||||||||
008			140830s2015 gw |||| o |||| 0|eng
010			_a 2019757897
020			_a97831319102467
040			_cDLC
050			_aQA76.9.D343 _bG373 2015
100	1		_aGarcía, Salvador, _eauthor. _92319
245	1	0	_aData Preprocessing in Data Mining / _cby Salvador García, Julián Luengo, Francisco Herrera.
250			_a1st ed. 2015.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015.
300			_aXV, 320 p. : _bill; _c24 cm
505	0		_aIntroduction -- 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			_aData 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.
588			_aDescription based on publisher-supplied MARC data.
650		0	_aComputational intelligence. _92320
650		0	_aData mining. _9805
650		0	_aOptical data processing. _92321
650	1	4	_aComputational Intelligence. _0https://scigraph.springernature.com/ontologies/product-market-codes/T11014 _92322
650	2	4	_aData Mining and Knowledge Discovery. _0https://scigraph.springernature.com/ontologies/product-market-codes/I18030 _92323
650	2	4	_aImage Processing and Computer Vision. _0https://scigraph.springernature.com/ontologies/product-market-codes/I22021 _92324
700	1		_aHerrera, Francisco _c(Computer scientist), _eauthor. _92325
700	1		_aLuengo, Julián, _eauthor. _92326
942			_2lcc _cBK
999			_c919 _d919