数据挖掘应用课件

数据挖掘应用CRM顾客生命周期寿命盈利

获取消费者保持消费者消费者分析和恢复收入支出寿命CustomeridentificationCRMbeginswithcustomeridentification.Thisphaseinvolvestargetingthepopulationwhoaremostlikelytobecomecustomersormostprofitabletothecompany.Italsoinvolvesanalyzingcustomerswhoarebeinglosttothecompetitionandhowtheycanbewonback.Elementsforcustomeridentificationincludetargetcustomeranalysisandcustomersegmentation.CustomerattractionOrganizationscandirecteffortandresourcesintoattractingthetargetcustomersegments.Directmarketingisapromotionprocesswhichmotivatescustomerstoplaceordersthroughvariouschannels.directmailorcoupon目标营销客户流失分析CustomerdevelopmentElementsofcustomerdevelopmentincludecustomerlifetimevalueanalysis,up/crosssellingandmarketbasketanalysis.Customerlifetimevalueanalysisisdefinedasthepredictionofthetotalnetincomeacompanycanexpectfromacustomer.Up/Crosssellingreferstopromotionactivitieswhichaimataugmentingthenumberofassociatedorcloselyrelatedservicesthatacustomeruseswithinafirm.Marketbasketanalysisaimsatmaximizingthecustomertransactionintensityandvaluebyrevealingregularitiesinthepurchasebehaviourofcustomers.Personalizedrecommendationsystems

Informationfilteringandrecommendationrule-basedfiltering,content-basedfiltering,andcollaborativefiltering.Rule-basedfilteringusespre-specifiedif-thenrulestoselectrelevantinformationforrecommendation.Content-basedfilteringuseskeywordsorotherproduct-relatedattributestomakerecommendations.Collaborativefilteringusespreferencesofsimilarusersinthesamereferencegroupasabasisforrecommendation.TypicalpersonalizationprocessunderstandingcustomersthroughprodeliveringpersonalizedofferingbasedontheknowledgeabouttheproductandthecustomermeasuringpersonalizationimpactInadequateinformationinIROnepossiblesolutionforovercomingtheproblemistoexpandthequerybyaddingmoresemanticinformationtobetterdescribetheconcepts.Relevancefeedbacksandknowledgestructureareusedtoaddappropriatetermstoexpandthequeries.Relevancefeedbacksareinformationontheitemsselectedbytheuserfromtheoutputofpreviousqueries.Apersonalizedknowledgerecommendationsystem

Asemantic-expansionapproachtobuildtheuserproanalyzingdocumentspreviouslyreadbytheperson.Thesemantic-expansionapproachthatintegratessemanticinformationforspreadingexpansionandcontent-basedfilteringfordocumentrecommendation.Asamplesemantic-expansionnetworkExperimentalresultsAnempiricalstudyusingmasterthesesintheNationalCentrallibraryinTaiwanshowsthatthesemantic-expansionapproachoutperformsthetraditionalkeywordapproachincatchinguserinterests.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{windows}{windows,SQLServer}{Linux}{Linux,Mysql}{金融}{金融,SQLServer}{windows,金融}{windows,金融,SQLServer}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。采用决策树挖掘出人员选拔规则CHAIDDecisiontreeforpredictingjobperformanceImprovingeducationImprovingteachingandlearningInstructorscanhavetroubleidentifyingtheirrealdifficultiesinlearning.Basedonthestudents’testingrecords,thesystemworkstoidentifyandfindthoseproblems,andthencomesupwithitssuggestionsfordesigningnewteachingstrategies.Assistteacherstoidentifystudents’specificdifficultiesandweaknessesinlearning.Helpsthestudenttofindouthisorherweakpointsinlearningandoffersimprovementrecommendations.ESLrecommenderteachingandlearningRight/wronganswerstatisticaltableForeverystudent,thesystemcreatesaright/wronganswerstatisticaltable:awronganswerisrepresentedby1andarightanswerby0.Summarytableofstudents’wronganswersTheright/wronganswerstatisticaltablesforrespectivestudentsareintegratedinasummarytableofstudents’wronganswers,andthesumvaluesinthetablearethenrankedindescendingordersoastoshowthedescendingdegreesofweaknessesthestudentshavecollectively.HierarchicalclusteringHierarchicalclusteringalgorithmisthenappliedtodatacollectedtosegmentthestudentsintoacertainnumberofclusters,orcategories,eachofwhichincludesstudentssharingthesameorsimilarcharacteristics.Allstudents’right/wronganswerstatisticaltablesClusteringanalysisAclusteringanalysisismadeofthedatainAllstudents’right/wronganswerstatisticaltables.Itisevidentthatthestudentswhosenumbersareenclosedinthefollowingseparateparenthesesbelongtodifferentclustersrespectively:(9,15,6,17,13,19,14,5);(22,23,4,3,21,11,24,20,7,1);(12,18,2,8,25,10,16).搜索引擎优化搜索引擎优化Theyareusuallynotsearchenginesbythemselves.Theclusteringengineusesoneormoretraditionalsearchenginestogatheranumberofresults;then,itdoesaformofpost-processingontheseresultsinordertoclusterthemintomeaningfulgroups.Thepost-processingstepanalyzessnippets,i.e.,shortdocumentabstractsreturnedbythesearchengine,usuallycontainingwordsaroundquerytermoccurrences.研讨题阅读后面参考文献，分析案例使用的数据挖掘方法以及解决的主要问题。结合自己的实践，说明所在岗位对商务智能的需求（针对软件工程硕士）。典型参考文献(1)Chen-FuChien,Li-FeiChen.Dataminingtoimprovepersonnelselectionandenhancehumancapital:acasestudyinhigh-technologyindustry.ExpertSystemswithApplication,2008,(34):280-290Cristo´balRomero,Sebastia´nVentura,EnriqueGarcı´a.Dataminingincoursemanagementsystems:Moodlecasestudyandtutorial.Computers&Education51(2008)368–384Yang,C.C.etal.,Improvingschedulingofemergencyphysiciansusingdatamininganalysis,ExpertSystemswithApplications(2008),doi:10.1016/j.eswa.2008.02.069JangHeeLee,SangChanPark.Intelligentprofitablecustomerssegmentationsystembasedonbusinessintelligencetools.ExpertSystemswithApplications29(2005):145–152Chih-MingChen,Ying-LingHsieh,Shih-HsunHsu.Mininglearnerproassociationruleforweb-basedlearningdiagnosis.ExpertSystemswithApplications33(2007)6–22Bong-HorngVhu,Ming-ShianTsai,Cheng-SeenHo.Towardahybriddataminingmodelforcusterretention.Knowledge-BasedSystems20(2007)703–718DanielaGrigoria,FabioCasatib,MaluCastellanos,etal.Businessprocessintelligence.ComputersinIndustry53(2004)321–343DursunDelen,ChristieFuller,CharlesMcCann.Analysisofhealthcarecoverage:Adataminingapproach.Delen,D.etal.,Analysisofhealthcarecoverage:Adataminingapproach,ExpertSystems