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007			cr nn 008mamaa
008			140830s2015 gw | s |||| 0|eng d
020			_a9783319102474
024	7		_a10.1007/978-3-319-10247-4 _2doi
040			_bspa _dES-MaUEC
050		4	_aQA76.9.D343 _b2015 EB
100	1		_aGarcía, Salvador. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n2004146242 _1http://viaf.org/viaf/49247928/
245	1	0	_aData Preprocessing in Data Mining _cby Salvador García, Julián Luengo, Francisco Herrera.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XV, 320 páginas 41 ilustraciones)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aIntelligent Systems Reference Library, _x1868-4394 ; _v72
490	0		_aEngineering (Springer-11647)
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	3		_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.
988			_aEBSPRINGER_2018
650		7	_aData mining _2embne _9162648
700	1		_aLuengo, Julián. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/34111899/
700	1		_aHerrera, Francisco. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n85336219 _1http://viaf.org/viaf/100186624/ _1http://dbpedia.org/resource/Francisco_Herrera_Luque _945432
776	0	8	_iEdición impresa: _z9783319102481
776	0	8	_iEdición impresa: _z9783319102467
776	0	8	_iEdición impresa: _z9783319377315
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-10247-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2019 _dz _eIG _zSI