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1、基于决策树方法的分类规则的挖掘MiningclassificationrulesbasedondecisiontreemethodCategory:AUTO--HTMLFifthauthors:WangShuyanAbstract:Dataminingismotivatedbythe"decisionsupportproblem"problemfacedbysomelargeretailorganizations.Alargenumberofsalesdatacollectedbybarcodetechno
2、logyhasbecomethebasisofmining.Basedonthedatamining,wecanfindforsalesandproductionforsomeusefulinformation(theinformationreflectedbyacertainpattern),soastoimprovesalesandproductionefficiency,reducecosts,maximizebusinessbenefits,thisisthesignificanceofdatami
3、ning.Thispaperintroducesamodelofdatamining:classificationrulesbasedondecisiontreemethod.Andanalgorithmofconstructingdecisiontreeclassifier--C4.5algorithmisdetailed1researchbackground1.1termsDefinitionofKDDtechnology:KDDisaprocessofextractingcredible,novel,
4、effectiveandunderstandablepatternsfromalargeamountofdata.Thisprocessisanadvancedprocessingprocess.Generally,KDDconsistsofthreephases:datapreparation,datamining,andinterpretationandevaluationofresults.Dataminingisbasedonthedecisionneeds,determinethemissiona
5、ndpurposeofdatamining,andtheminingalgorithmfromdataminingusefulknowledgebyspecificdata,isthecorepartofKDD,isthemaintopicofKDDresearch.Ingeneral,wedon'tdistinguishbetweenKDDanddatamining.Datamining(DataMining)isfromthelarge,incomplete,noisy,fuzzyandrandomda
6、ta,inwhichtheextractionofimplicit,peopledonotknowinadvance,butispotentiallyusefulinformationandknowledge.Analysisof2classificationrules2.1classifierTheproblemofclassificationistocategorizeaneventoranobject.Inuse,themodelcanbeusedtoanalyzeexistingdata,ortop
7、redictfuturedata.Theconceptofclassificationistolearnaclassificationfunctiononthebasisofexistingdataortoconstructaclassificationmodel(thatis,whatweusuallyrefertoasaclassifier(Classifier)).Thefunctionormodelcanmapthedatarecordsinthedatabasetooneofthegivencla
8、sses,whichcanbeappliedtodataprediction.Forexample,usetheclassificationtopredictwhichcustomersaremostlikelytosellfordirectmailtorespond,andwhatthecustomermaychangehismobilephoneserviceprovider,orinthemedicalfi
