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ID:31983243
大小:5.95 MB
页数:71页
时间:2019-01-30
《基于数据挖掘的电信行业中客户流失模型的分析与实现》由会员上传分享,免费在线阅读,更多相关内容在教育资源-天天文库。
1、浙江理工大学硕士学位论文基于数据挖掘的电信行业中客户流失模型的研究与实现AbstractInrecentyears,customerchumintelecombecomesserious.ForthethreeoperatorsincludingMobile,ChinaUnicornandTelcomwhoCallassuretheircustomersnottoabandonthedefaultgotoanotherone,atthesametimecangetthelostfromothers,wil
2、lbethefinalwinnerAbittercontesthaslaunchedamongoperatorswhodoalltheycouldtogainmorecustomers.However,operatorspaymuchmoreattentiontocustomerschurnedratherthanthosewhoarereducingtheirconsumption.Hardlyrealizethatthosepeoplearerunningoffgraduallyaspotential
3、lOSScustomers.Telecommunicationsindus仃),hasamassofdataanddiversitywhichmeansthateverycustomerhasalargenumberofattributeswhichcalledvariablesinthedataminingmodel,suchasARPU,chargeway,downtimesetc.Inordertobettermodel,thispaperdesignswidesheetfromthreeaspec
4、tsofoperatorsendsmessagestoremindcustomer,customerperceptivevalueandvaluebehaviorofcustomerbasedontheassumptionthatthereasonsforcustomerschum.Thendividesthesevariablesintodifferentgroupstodecidewhichvariablestoparticipateinmodelmodeling.Combinedthecustome
5、r’Sowncharacteristicsandthegroupingvariablesthensubdivideallofthecustomers.Customer-chummodelbecomesmoreandmoreintelecommunicationsindustry,inordertoimprovethehitrateofthemodel,thispaperproposesacombinedmodelphilosophy.Thecombinedmodelisbasedontheconstrai
6、ntmodel,predictionmodel,markmodel.Constraintmodelselectsvariableshaslargedistinctiontobeconstraintconditions,predictionmodelscreensvisiblelossandrelativelyobviousvariables,markmodelselectsimplicitcustomerchumvariablestomakeupsmallamountofsamplestoidentify
7、customermorecomprehensively.Eachmodelhasitsownspecialvariables,asaresult,thecombinationmodelproposedinthispaperplaysasignificantroleincustomer-chummodel.ThismodelusesIBMSPSSStatistics,putsforwardtheconceptofpheromonedifferenceinantcolonyalgorithm,usingthe
8、improvedantcolonyalgorithmtosubdividethecustomertoimprovethecustomerclusteringeffect.Canonicaltransformationeliminatetheimpactofthedimensionlesscoefficientstomakethemodelmoreregular.Usinglogisticregressionalgorithmi
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