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1、FuzzySetsinMachineLearningandDataMiningEykeHullermeier•Philipps-Universit•atMarburgDepartmentofMathematicsandComputerScienceMarburg,Germanyeyke@informatik.uni-marburg.deAbstractMachinelearning,datamining,andseveralrelatedresearchareasareconcernedwithmethodsfortheautoma
2、tedinductionofmodelsandtheextractionofinter-estingpatternsfromempiricaldata.Automatedknowledgeacquisitionofthatkindhasbeenanessentialaspectofarticialintelligencesincealongtimeandhasmorerecentlyalsoattractedconsiderableattentioninthefuzzysetscommunity.Thispa-perbrie
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3、eviewssometypicalapplicationsandhighlightspotentialcontributionsthatfuzzysettheorycanmaketomachinelearning,datamining,andrelatedelds.Inthisconnection,someadvantagesoffuzzymethodsforrepresentingandminingvaguepatternsindataareespeciallyemphasized.1IntroductionAstheconce
4、ptionofintelligenceisinseparablyconnectedwiththeabilitytolearnfromexperienceandtoadapttonewsituations,itishardlyastonish-ingthatmachinelearninghasalwaysbeenconsideredasanintegralpartoftheeldofarticialintelligence(AI).Infact,thekeyroleoflearningandadaptationisalmostse
5、lf-evidentfortheconnectionistapproachtoAI,sincerepresentingknowledgewith(articial)neuralnetworksbecomespossibleonlybytraining"suchnetworks.However,inviewofthefactthattheknowl-edgeacquisitionbottleneck"turnedouttobeoneofthekeyproblemsinthedesignofintelligentandknowle
6、dge-basedsystems,theimportanceofau-tomatedmethodsforknowledgeacquisitionhasalsobeenrealizedformoresymbol-orientedapproacheslikerule-basedsystems(forwhichexpertknowl-edgecouldinprinciplebeinjectedbyhand).Inanycase,machinelearningiscertainlyoneofthemostimportantsubeldso
7、fcontemporaryAI,regardlessoftheparticularfacet.1Aresearcheldcloselyrelatedtomachinelearningisthatofknowledgediscov-eryanddatamining.Asaresponsetotheprogressindigitaldataacquisitionandstoragetechnology,alongwiththelimitedhumancapabilitiesinanalyzingandexploitinglargeam
8、ountsofdata,thiseldhasrecentlyemergedasanewresearchdiscipline,lyingattheintersectionofstatistics,machinelearning,dat