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001 978-3-319-10247-4
003 DE-He213
005 20200421112040.0
007 cr nn 008mamaa
008 140830s2015 gw | s |||| 0|eng d
020 _a9783319102474
_9978-3-319-10247-4
024 7 _a10.1007/978-3-319-10247-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aGarc�ia, Salvador.
_eauthor.
245 1 0 _aData Preprocessing in Data Mining
_h[electronic resource] /
_cby Salvador Garc�ia, Juli�an Luengo, Francisco Herrera.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXV, 320 p. 41 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v72
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.
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aImage processing.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aImage Processing and Computer Vision.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aLuengo, Juli�an.
_eauthor.
700 1 _aHerrera, Francisco.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319102467
830 0 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v72
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-10247-4
912 _aZDB-2-ENG
942 _cEBK
999 _c56587
_d56587