LEADER 02970nam 2200577 450 001 9910260649903321 005 20221206230726.0 010 $a0-262-30408-2 010 $a0-262-25630-4 010 $a1-282-09636-2 010 $a1-4237-3132-8 035 $a(CKB)111035898479412 035 $a(CaBNVSL)mat06267275 035 $a(IDAMS)0b000064818b4261 035 $a(IEEE)6267275 035 $a(SSID)ssj0000227326 035 $a(PQKBManifestationID)11185225 035 $a(PQKBTitleCode)TC0000227326 035 $a(PQKBWorkID)10264381 035 $a(PQKB)11330412 035 $a(MiAaPQ)EBC6243328 035 $a(EXLCZ)99111035898479412 100 $a20151228d2001 uy 101 0 $aeng 135 $aur|n||||||||| 181 $2rdacontent 182 $2isbdmedia 183 $2rdacarrier 200 10$aPrinciples of data mining /$fDavid Hand, Heikki Mannila, Padhraic Smyth 210 1$aCambridge, Massachusetts :$cMIT Press,$d2001. 210 2$a[Piscataqay, New Jersey] :$cIEEE Xplore,$d[2001] 215 $a1 PDF (xxxii, 546 pages) 225 1 $aAdaptive computation and machine learning series 300 $a"A Bradford book." 311 $a0-262-08290-X 320 $aIncludes bibliographical references (p. [491]-524) and index. 330 $aThe growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics.The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing. 410 0$aAdaptive computation and machine learning series 606 $aData mining 615 0$aData mining. 676 $a006.312 700 $aHand$b D. J.$0103948 701 $aMannila$b Heikki$0145716 701 $aSmyth$b Padhraic$0145717 801 0$bCaBNVSL 801 1$bCaBNVSL 801 2$bCaBNVSL 906 $aBOOK 912 $a9910260649903321 996 $aPrinciples of data mining$9507428 997 $aUNINA