LEADER 03115nam 2200685 a 450 001 9910822512703321 005 20240516082804.0 010 $a1-283-14352-6 010 $a9786613143525 010 $a981-281-468-X 035 $a(CKB)2490000000001940 035 $a(EBL)731074 035 $a(OCoLC)738438068 035 $a(SSID)ssj0000524508 035 $a(PQKBManifestationID)12210352 035 $a(PQKBTitleCode)TC0000524508 035 $a(PQKBWorkID)10562227 035 $a(PQKB)11359020 035 $a(MiAaPQ)EBC731074 035 $a(WSP)00006861 035 $a(Au-PeEL)EBL731074 035 $a(CaPaEBR)ebr10480244 035 $a(CaONFJC)MIL314352 035 $a(EXLCZ)992490000000001940 100 $a20110224d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aNonlinear integrals and their applications in data mining /$fZhenyuan Wang, Rong Yang, Kwong-Sak Leung 205 $a1st ed. 210 $aSingapore ;$aHackensack, N.J. $cWorld Scientific$dc2010 215 $a1 online resource (360 p.) 225 1 $aAdvances in fuzzy systems : applications and theory ;$vv. 17 [i.e. 24] 300 $aDescription based upon print version of record. 311 $a981-281-467-1 320 $aIncludes bibliographical references and index. 327 $aPreface; Contents; List of Tables; List of Figures; Chapter 1: Introduction; Chapter 2: Basic Knowledge on Classical Sets; Chapter 3: Fuzzy Sets; Chapter 4: Set Functions; Chapter 5: Integrations; Chapter 6: Information Fusion; Chapter 7: Optimization and Soft Computing; Chapter 8: Identification of Set Functions; Chapter 9: Multiregression Based on Nonlinear Integrals; Chapter 10: Classifications Based on Nonlinear Integrals; Chapter 11: Data Mining with Fuzzy Data; Bibliography; Index 330 $aRegarding the set of all feature attributes in a given database as the universal set, this monograph discusses various nonadditive set functions that describe the interaction among the contributions from feature attributes towards a considered target attribute. Then, the relevant nonlinear integrals are investigated. These integrals can be applied as aggregation tools in information fusion and data mining, such as synthetic evaluation, nonlinear multiregressions, and nonlinear classifications. Some methods of fuzzification are also introduced for nonlinear integrals such that fuzzy data can be 410 0$aAdvances in fuzzy systems ;$vv. 24. 606 $aFuzzy sets 606 $aIntegrals 606 $aFuzzy logic 606 $aData mining 615 0$aFuzzy sets. 615 0$aIntegrals. 615 0$aFuzzy logic. 615 0$aData mining. 676 $a511.313 700 $aWang$b Zhenyuan$027994 701 $aYang$b Rong$01702038 701 $aLeung$b Kwong Sak$f1955-$01702039 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910822512703321 996 $aNonlinear integrals and their applications in data mining$94086261 997 $aUNINA