英文原文毕业设计（论文）外文翻译题目对零售超市数据进行最优产品选择的数据挖掘框架：广义PROFSET模型专业网络工程班级041班学生朱朋伟指导教师李薇2008年外文翻译附录英文原文ADataMiningFrameworkforOptimalProductSelectioninRetailSupermarketData:TheGeneralizedPROFSETModel1IntroductionSincealmostallmidtolargesizeretailerstodaypossesselectronicsalestransactionSystems,retailersrealizethatcompetitiveadvantagewillnolongerbeachievedbythemereuseofthesesystemsforpurposesofinventorymanagementorfacilitatingcustomercheck-out.Incontrast,competitiveadvantagewillbegainedbythoseretailerswhoareabletoextracttheknowledgehiddeninthedata,generatedbythosesystems,anduseittooptimizetheirmarketingdecisionmaking.Inthiscontext,knowledgeabouthowcustomersareusingtheretailstoreisofcriticalimportanceanddistinctivecompetencieswillbebuiltbythoseretailerswhobestsucceedinextractingactionableknowledgefromthesedata.Associationrulemining[2]canhelpretailerstoefficientlyextractthisknowledgefromlargeretaildatabases.Weassumesomefamiliaritywiththebasicnotionsofassociationrulemining.Inrecentyears,alotofeffortintheareaofretailmarketbasketanalysishasbeeninvestedinthedevelopmentoftechniquestoincreasetheinterestingnessofassociationrules.Currently,inessencethreedifferentresearchtrackstostudytheinterestingnessofassociationrulescanbedistinguished.First,anumberofobjectivemeasuresofinterestingnesshavebeendevelopedinordertofilteroutnon-interestingassociationrulesbasedonanumberofstatisticalpropertiesoftherules,suchassupportandconfidence[2],interest[14],intensityofimplication[7],J-measure[15],andcorrelation[12].Othermeasuresarebasedonthesyntacticalpropertiesoftherules[11],ortheyareusedtodiscovertheleast-redundantsetofrules[4].Second,itwasrecognizedthatdomainknowledgemayalsoplayanimportantroleindeterminingtheinterestingnessofassociationrules.Therefore,anumberofsubjectivemeasuresofinterestingnesshavebeenputforward,suchasunexpectedness[13],actionability[1]andruletemplates[10].Finally,themostrecentstreamofresearchadvocatestheevaluationoftheinterestingnessofassociationsin