LEADER 04426nam 22006975 450 001 9910299844903321 005 20200706082145.0 010 $a3-319-10247-8 024 7 $a10.1007/978-3-319-10247-4 035 $a(CKB)3710000000227373 035 $a(EBL)1968256 035 $a(OCoLC)890468082 035 $a(SSID)ssj0001338550 035 $a(PQKBManifestationID)11813708 035 $a(PQKBTitleCode)TC0001338550 035 $a(PQKBWorkID)11345250 035 $a(PQKB)10934824 035 $a(DE-He213)978-3-319-10247-4 035 $a(MiAaPQ)EBC1968256 035 $a(PPN)180628070 035 $a(EXLCZ)993710000000227373 100 $a20140830d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData Preprocessing in Data Mining /$fby Salvador García, Julián Luengo, Francisco Herrera 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (327 p.) 225 1 $aIntelligent Systems Reference Library,$x1868-4394 ;$v72 300 $aDescription based upon print version of record. 311 $a3-319-10246-X 320 $aIncludes bibliographical references and index. 327 $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. 330 $aData Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering. 410 0$aIntelligent Systems Reference Library,$x1868-4394 ;$v72 606 $aComputational intelligence 606 $aOptical data processing 606 $aData mining 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aComputational intelligence. 615 0$aOptical data processing. 615 0$aData mining. 615 14$aComputational Intelligence. 615 24$aImage Processing and Computer Vision. 615 24$aData Mining and Knowledge Discovery. 676 $a006.3 676 $a006.312 676 $a006.37 676 $a006.6 700 $aGarcía$b Salvador$4aut$4http://id.loc.gov/vocabulary/relators/aut$0384993 702 $aLuengo$b Julián$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aHerrera$b Francisco$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910299844903321 996 $aData Preprocessing in Data Mining$92515640 997 $aUNINA