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一种优化的关联规则增量更新算法.docx

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一种优化的关联规则增量更新算法 Title:AnOptimizedAlgorithmforIncrementalUpdateofAssociationRules Abstract: Associationruleminingisapopulartechniqueusedindataminingandmachinelearningfordiscoveringinterestingrelationshipsorpatternsinlargedatasets.Inordertokeepassociationrulesuptodatewiththeever-changingdata,anefficientincrementalupdatealgorithmbecomescrucial.Thispaperproposesanoptimizedalgorithmforupdatingassociationrulesincrementally,aimingtoreducecomputationalcomplexityandimproveperformance. 1.Introduction: Associationrulemininghasawiderangeofapplicationsinvariousdomainssuchasmarketbasketanalysis,customerbehavioranalysis,andrecommendationsystems.Thestandardapproachinvolvesgeneratingassociationrulesfromagivendatasetformakingpredictionsorgeneratinginsights.However,asdatacontinuouslyevolves,theassociationrulesmustbeupdatedtoreflectthelatestpatternsaccurately.Thispaperpresentsanoptimizedalgorithmforefficientlyupdatingassociationrulesincrementally,byconsideringtherelationshipsbetweenoldandnewdata. 2.RelatedWork: Previousstudieshaveproposeddifferentmethodsforupdatingassociationrulesincrementally.Someresearchershavefocusedonusingincrementalminingalgorithms,whileothershaveexploredtechniquesbasedontheconceptofdatabasedifferencing.However,thesemethodseithersufferfromhighcomputationalcomplexityordonottakefulladvantageoftherelationshipsbetweenoldandnewdata.Thispaperaimstoaddresstheselimitationsandprovideamoreefficientapproachforincrementalassociationruleupdates. 3.ProposedAlgorithm: Theproposedalgorithmconsistsofseveralsteps,namelydatapreprocessing,rulegeneration,andruleupdate.Inthedatapreprocessingstep,thealgorithmexaminesboththeoldandnewdatasetstoidentifycommonpatternsanddifferences.Thisisachievedbyutilizingappropriatedatastructures,suchashashtablesandindexes,toefficientlystoreandaccessthetransactionaldata. Next,thealgorithmgeneratesassociationrulesfromtheolddatasetusingarulegenerationtechnique,suchastheApriorialgorithm.Theserulesactasastartingpointfortheincrementalupdateprocess. Intheruleup