数据挖掘应用ppt课件优质PPT.ppt

数据挖掘应用ppt课件优质PPT.ppt

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数据挖掘应用,CRM,顾客生命周期,数据挖掘在CRM中的应用,Customeridentification,CRMbeginswithcustomeridentification.Thisphaseinvolvestargetingthepopulationwhoaremostlikelytobecomecustomersormostprofitabletothecompany.Italsoinvolvesanalyzingcustomerswhoarebeinglosttothecompetitionandhowtheycanbewonback.Elementsforcustomeridentificationincludetargetcustomeranalysisandcustomersegmentation.,Customerattraction,Organizationscandirecteffortandresourcesintoattractingthetargetcustomersegments.Directmarketingisapromotionprocesswhichmotivatescustomerstoplaceordersthroughvariouschannels.directmailorcoupon,目标营销,Customerretention,CentralconcernforCRM.Customersatisfactionistheessentialconditionforretainingcustomers.Elementsofcustomerretentionincludeone-to-onemarketing,loyaltyprogramsandcomplaintsmanagement.One-to-onemarketingreferstopersonalizedmarketingcampaignswhicharesupportedbyanalyzing,detectingandpredictingchangesincustomerbehaviors.Loyaltyprogramsinvolvecampaignsorsupportingactivitieswhichaimatmaintainingalongtermrelationshipwithcustomers.Churnanalysis,creditscoring,servicequalityorsatisfactionformpartofloyaltyprograms.,客户流失分析,Customerdevelopment,Elementsofcustomerdevelopmentincludecustomerlifetimevalueanalysis,up/crosssellingandmarketbasketanalysis.Customerlifetimevalueanalysisisdefinedasthepredictionofthetotalnetincomeacompanycanexpectfromacustomer.Up/Crosssellingreferstopromotionactivitieswhichaimataugmentingthenumberofassociatedorcloselyrelatedservicesthatacustomeruseswithinafirm.Marketbasketanalysisaimsatmaximizingthecustomertransactionintensityandvaluebyrevealingregularitiesinthepurchasebehaviourofcustomers.,Personalizedrecommendationsystems,Personalizedrecommendation,Personalizationisdefinedas“theabilitytoprovidecontentandservicestailoredtoindividualsbasedonknowledgeabouttheirpreferencesandbehavior”or“theuseoftechnologyandcustomerinformationtotailorelectroniccommerceinteractionsbetweenabusinessandeachindividualcustomer”Internetrecommendationsystems（Internetrecommendersystems）inelectroniccommerceistoreduceirrelevantcontentandprovideuserswithmorepertinentinformationorproduct.Arecommendationsystemisacomputer-basedsystemthatusesprofilesbuiltfrompastusagebehaviortoproviderelevantrecommendations.,Informationfilteringandrecommendation,rule-basedfiltering,content-basedfiltering,andcollaborativefiltering.Rule-basedfilteringusespre-specifiedif-thenrulestoselectrelevantinformationforrecommendation.Content-basedfilteringuseskeywordsorotherproduct-relatedattributestomakerecommendations.Collaborativefilteringusespreferencesofsimilarusersinthesamereferencegroupasabasisforrecommendation.,Typicalpersonalizationprocess,understandingcustomersthroughprofilebuildingdeliveringpersonalizedofferingbasedontheknowledgeabouttheproductandthecustomermeasuringpersonalizationimpact,InadequateinformationinIR,Onepossiblesolutionforovercomingtheproblemistoexpandthequerybyaddingmoresemanticinformationtobetterdescribetheconcepts.Relevancefeedbacksandknowledgestructureareusedtoaddappropriatetermstoexpandthequeries.Relevancefeedbacksareinformationontheitemsselectedbytheuserfromtheoutputofpreviousqueries.,SpreadingActivationModel,IntheSpreadingActivation（SA）Model,conceptsareexpandedbasedonthesemanticsintheprocessofidentifyingcustomerprofileandmatchingitemsandthemodelhasbeenappliedtoexpandqueries.,Apersonalizedknowledgerecommendationsystem,Asemantic-expansionapproachtobuildtheuserprofilebyanalyzingdocumentspreviouslyreadbytheperson.Thesemantic-expansionapproachthatintegratessemanticinformationforspreadingexpansionandcontent-basedfilteringfordocumentrecommendation.,Asamplesemantic-expansionnetwork,Experimentalresults,AnempiricalstudyusingmasterthesesintheNationalCentrallibraryinTaiwanshowsthatthesemantic-expansionapproachoutperformsthetraditionalkeywordapproachincatchinguserinterests.,构件库管理,自适应构件检索,构件检索是构件库研究中的重要问题，有效的构件检索机制能够降低构件复用成本。

@#@构件的复用者并不是构件的设计者或构件库的管理员，在检索构件时对构件库的描述理解不充分，导致难以给出完整和精确的检索需求。

@#@用户选择构件的结果反映其真实需求，如果能够从用户的检索行为以及用户对检索结果的反馈中推断出用户的非精确检索条件与用户实际需要的精确检索条件之间内在联系的模式，就可以提高系统的查准率。

@#@,基于关联挖掘的自适应构件检索,把关联规则挖掘方法引入构件检索，从用户检索行为以及反馈中挖掘出非精确检索条件与精确检索结果之间的关联规则，从而调整检索机制，提高构件检索的查准率。

@#@,实例,windowswindows,SQLServerLinuxLinux,Mysql金融金融,SQLServerwindows,金融windows,金融,SQLServer,供应链管理,零部件供应商选择,如何选择供应商不仅决定了产品的质量和成本,也决定了产品的销售价格、维护费用和用户满意程度。

@#@选择供应商一般以满足时间约束的条件下最小化物流成本为目标，没有考虑零部件故障率与不同地域环境之间的相关性。

@#@,基于关联规则的零部件供应商选择,使用关联规则挖掘算法，从产品维修记录中，寻找不同供应商提供的产品零部件及其组合在不同地域的频繁故障模式。

@#@在生成供应商选择和配送方案过程中，利用这些频繁故障模式，选择合适的零部件供应商组合，达到物流成本与产品维护成本的联合优化。

@#@,人力资源管理,人力资源管理,人力资源在高科技公司中的地位相当重要。

@#@人力招聘直接影响公司员工的素质，但传统的人力资源管理方法已经不适应