數(shù)據(jù)挖掘應(yīng)用CRM顧客生命周期壽命盈利

獲取消費(fèi)者保持消費(fèi)者消費(fèi)者分析和恢復(fù)收入支出壽命CustomeridentificationCRMbeginswithcustomeridentification.Thisphaseinvolvestargetingthepopulationwhoaremostlikelytobecomecustomersormostprofitabletothecompany.Italsoinvolvesanalyzingcustomerswhoarebeinglosttothecompetitionandhowtheycanbewonback.Elementsforcustomeridentificationincludetargetcustomeranalysisandcustomersegmentation.CustomerattractionOrganizationscandirecteffortandresourcesintoattractingthetargetcustomersegments.Directmarketingisapromotionprocesswhichmotivatescustomerstoplaceordersthroughvariouschannels.directmailorcoupon目標(biāo)營銷客戶流失分析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.自適應(yīng)構(gòu)件檢索構(gòu)件檢索是構(gòu)件庫研究中的重要問題，有效的構(gòu)件檢索機(jī)制能夠降低構(gòu)件復(fù)用成本。構(gòu)件的復(fù)用者并不是構(gòu)件的設(shè)計者或構(gòu)件庫的管理員，在檢索構(gòu)件時對構(gòu)件庫的描述理解不充分，導(dǎo)致難以給出完整和精確的檢索需求。用戶選擇構(gòu)件的結(jié)果反映其真實需求，如果能夠從用戶的檢索行為以及用戶對檢索結(jié)果的反饋中推斷出用戶的非精確檢索條件與用戶實際需要的精確檢索條件之間內(nèi)在聯(lián)系的模式，就可以提高系統(tǒng)的查準(zhǔn)率?；陉P(guān)聯(lián)挖掘的自適應(yīng)構(gòu)件檢索把關(guān)聯(lián)規(guī)則挖掘方法引入構(gòu)件檢索，從用戶檢索行為以及反饋中挖掘出非精確檢索條件與精確檢索結(jié)果之間的關(guān)聯(lián)規(guī)則，從而調(diào)整檢索機(jī)制，提高構(gòu)件檢索的查準(zhǔn)率。實例{windows}{windows,SQLServer}{Linux}{Linux,Mysql}{金融}{金融,SQLServer}{windows,金融}{windows,金融,SQLServer}零部件供應(yīng)商選擇如何選擇供應(yīng)商不僅決定了產(chǎn)品的質(zhì)量和成本,也決定了產(chǎn)品的銷售價格、維護(hù)費(fèi)用和用戶滿意程度。選擇供應(yīng)商一般以滿足時間約束的條件下最小化物流成本為目標(biāo)，沒有考慮零部件故障率與不同地域環(huán)境之間的相關(guān)性。基于關(guān)聯(lián)規(guī)則的零部件供應(yīng)商選擇使用關(guān)聯(lián)規(guī)則挖掘算法，從產(chǎn)品維修記錄中，尋找不同供應(yīng)商提供的產(chǎn)品零部件及其組合在不同地域的頻繁故障模式。在生成供應(yīng)商選擇和配送方案過程中，利用這些頻繁故障模式，選擇合適的零部件供應(yīng)商組合，達(dá)到物流成本與產(chǎn)品維護(hù)成本的聯(lián)合優(yōu)化。采用決策樹挖掘出人員選拔規(guī)則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).搜索引擎優(yōu)化搜索引擎優(yōu)化Theyareusuallynotsearchenginesbythemselves.Theclusteringengineusesoneormoretraditionalsearchenginestogatheranumberofresults;then,itdoesaformofpost-processingontheseresultsinordertoclusterthemintomeaningfulgroups.Thepost-processingstepanalyzessnippets,i.e.,shortdocumentabstractsreturnedbythesearchengine,usuallycontainingwordsaroundquerytermoccurrences.研討題閱讀后面參考文獻(xiàn)，分析案例使用的數(shù)據(jù)挖掘方法以及解決的主要問題。結(jié)合自己的實踐，說明所在崗位對商務(wù)智能的需求（針對軟件工程碩士）。典型參考文獻(xiàn)(1)Chen-FuChien,Li-FeiChen.Dataminingtoimprovepersonnelselectionandenhancehumancapital:acasestudyinhigh-technologyindustry.ExpertSystemswithApplication,2008,(34):280-290Cristo′balRomero,Sebastia′nVentura,EnriqueGarc?′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