数据挖掘introduction管理学教授财经信息管理与工程学院

Data—Data-Mining Prof.Dr.Prof.Dr.LIUPagePageProf.PageProf.Dr.LIUPageProf.PageProf.Dr.LIU

概率 Prof.Dr.Prof.Dr.LIUPage Prof.Dr.Prof.Dr.LIUPagePagePageProf.Prof.Dr.LIUProf.Dr.Prof.Dr.LIUPage(First-principlemodels–0

TheDataTotalnewdisk(TB)since Grossman,C.informationoverloadbehappywithlargedataProf.Dr.Prof.Dr.LIUPageProf.PageProf.Dr.LIU“NecessityistheMotherof对大型的、复杂的、信息丰富的数据集的理解是几乎所有运用基于计算机的方法（包括新技术）从数据中获得有用PageProf.PageProf.Dr.LIUTurningaproduct-focusedorganizationintoacustomer-centriconetakesmorethandataminingPageProf.PageProf.Dr.LIU产品SAS(EnterpriseMinerIBMIntelligentMiner,SPSS(Clementine),Megaputer(PolyAnalyst),……DataminingisnotjustaboutproducingtechnicalEnergyKnowledgeCreativityprofessionalcreativethinkingawillingnesstoseeproblemsinadifferent可伸缩性高维性（High异构数据和复杂数据（HeterogeneousandComplex数据所有权与分布（DataOwnershipand

Prof.Dr.Prof.Dr.LIUPageP(是)=P(是|红球P(红球+P(是|白球P(白球Garbagein,garbagePageProf.Dr.PageProf.Dr.LIU模型集（modelThemodelcanonlybeasgoodasthedatausedtocreateit.代表了过去用来吸引客户的信息、优惠以及

Prof.Dr.Prof.Dr.LIUPageProf.Dr.LIUProf.Dr.LIUPageAstudyofcustomerattrition客户流失mayshowthatastrongpredictorofcustomerleavingisthewaytheywereacquired(获取).Itistoolatetogobackandchangethatforexistingcustomers,butthatdoesnotmaketheinformationuselessFutureattritioncanbereducedbychangingthemixofacquisitionchannelstofavorthosethatbringinlonger-lastingcustomers.aniterativeprogressisdefinedbythrougheitherautomaticormanualmostusefulinanexploratoryanalysisnopredeterminednotionsaboutwhatwillconstitutean“interesting”outcome.Dataminingisthesearchfornew,valuable,andnontrivialinformationinlargevolumesofdata.BestresultsareachievedbytheknowledgeofhumanexpertsindescribingproblemsandgoalsthesearchcapabilitiesofProf.Dr.Prof.Dr.LIUPage定义：数据挖掘是从已知数据集合中发现各种模型、概（DataMiningisaprocessofdiscoveringvariousmodels,summaries,andderivedvaluesfromagivencollectionofdata.）DataminingisaniterativeThegeneralexperimentalprocedureadaptedtodata-miningproblemsinvolvesthefollowingsteps:StatetheproblemandformulatetheDomain-specificknowledgeandexperienceareusuallyProf.Dr.Prof.Dr.LIUPageCollecttheTherearetwodistinctDesignedObservationalapproach.–randomdataPreprocessingtheTwocommonOutlierdetection(andScaling,encoding,andselectingtwodatapreparationEstimatethetheselectionandimplementationoftheappropriatedata-miningtechnique.InterpretthemodelanddrawData-miningmodelsshouldhelpindecisionModelsneedtobeinterpretableinordertobeAgoodunderstandingofthewholeprocessisimportantforanysuccessfulDataMiningandBusinesstosupportbusiness DataDataInformation

EndDataStatisticalAnalysis,QueryingandDataWarehouses/DataDataPaper,Files,InformationProviders,DatabaseSystems,

Prof.Dr.Prof.Dr.LIUPage结构化数据（structuredwell-definedfieldswithnumericoralphanumeric半结构化数据（semi-structuredelectronicimagesofbusinessdocuments,medicalreports,executivesummaries,andrepairmanualsThemajorityofwebavideorecordedbyasurveillancecamerainadepartment传统型数据（traditionalstructuredMostofthecurrentdataminingmethodsandcommercialtoolsareappliedto非传统型数据：semi-structuredandunstructurednontraditional(alsocalledmultimediathedevelopmentofdata-miningtoolsisprogressingatarapidratethestandardmodelofstructuredacollectionofinatabularform,orintheformofasinglePagePageProf.Dr.LIUindependentvariables(inputvariables)–dependentvariables(outputvariables)–additionalvariables(unobservedvariables)–influencesystembehavior,butthecorrespondingvaluesarenotThehighcomplexityandthecostofmeasurementsforthesenotundersta