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1、ICPSRSummerProgram,2012DataMiningToolsforExploringLargeDatasetsRobertStineDepartmentofStatisticsWhartonSchool,UniversityofPennsylvaniawww-stat.wharton.upenn.edu/~stineModerndataminingcombinesfamiliarandnovelstatisticalmethodstoidentifyreproduciblepatternsi
2、nwidedatasets.Widedatasetsaredistinguishedbyhavingmanycolumns(orvariables),oftenmorecolumnsthanrows.Theobjectiveinthisdomainisprediction.Ifyoucanpredictnewdataaccuratelyorbetterthanalternatives,thenyou’vemadeacontribution.Ratherthanbuildingamodelthatrelate
3、soneortwoexperimentalresultstoaresponse,datamininginvolvessearchingforpatterns.Suchsearchesmayscanthousandsoffeatures,lookingforthefewthatarepredictiveoftheresponse.Thesearchmightbeentirelyautomatedorallowexpertinsight.Onceavilethingtobeaccusedof,dataminin
4、ghasbecomerespectable,useful,andnecessary.Theselecturesintroducedataminingthroughacombinationoflecturesandexamples.You’llseeexamplesthatlookforpatternsinvotingbehavior,patientsatriskofadisease,prospectivejobcandidates,andcreditapplicationsthatrevealfraud.I
5、neachillustration,thegoalisprediction.Ratherthaninterpretapatternfoundinonesetofdata,theobjectiveistopredictnewdata.Interpretationisfun,butwe’llexerciseconsiderablerestrainttoavoidconfusingassociationwithcausation.Evenifyousticktosimplemodels,conceptsfromd
6、ataminingcanhelpdeterminewhetheryou’vemissedanimportantfeatureofyourdata.Dataminingdoesnotrequireexotichardwareorsoftware.Today’sPCwouldhavebeenasupercomputerin1999.Youcanexplorelargedatasetsquitewellwithnothingmorethanregressionandalaptop.That’sbasicallyw
7、hatwe’lldointhefirstweekofclass.Onceyougraspthefundamentals,you’llappreciatethestrengthsandweaknessesofexoticmethods.We’llstartwithregression,andthenlogisticregression,classificationandregressiontrees,andabitofneuralnetworksandclusteranalysis.Youneedtododa
8、taminingtolearndatamining.Forthisclass,we’lluseacombinationofRandJMPfromSAS.JMPhandlesverylargedatasetsandincludesanextensivecollectionofalgorithmsforbuildingandassessingregressionmodelswithdataminingtoolssuc
