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大数据环境下基于Hadoop框架的改进Apriori挖掘算法（英文）.docx

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大数据环境下基于Hadoop框架的改进Apriori挖掘算法（英文） Title:ImprovementofAprioriMiningAlgorithmBasedonHadoopFrameworkinBigDataEnvironment Abstract: Withtheproliferationofbigdata,traditionaldataminingalgorithmsfacethechallengeofprocessinglarge-scaledatasetsefficientlyandaccurately.Apriorialgorithm,asoneofthemostpopularassociationruleminingalgorithms,suffersfromperformanceissueswhenappliedtobigdataduetoitshighcomputationalcomplexity.Toaddressthischallenge,thispaperproposesanimprovementoftheApriorialgorithmbasedontheHadoopframework,whichisspecificallydesignedforbigdataprocessing.TheproposedapproachaimstoenhancetheefficiencyandscalabilityoftheApriorialgorithm,enablingittohandlelarge-scaledatasetsmoreeffectively. 1.Introduction Theexponentialgrowthofdatainrecentyearshasposedsignificantchallengestothetraditionaldataminingtechniques.Bigdatabringsunprecedentedopportunitiesforknowledgediscovery,butalsonecessitatesthedevelopmentofnewalgorithmsandframeworkscapableofhandlingthevastamountofinformation.Inthiscontext,theApriorialgorithm,widelyusedforassociationrulemining,requiresimprovementstocopewiththerequirementsofbigdata. 2.Background 2.1AprioriAlgorithm TheApriorialgorithmisaclassicassociationruleminingalgorithmthatidentifiesfrequentitemsetsfromlargedatasets.Itemploysabreadth-firstsearchstrategytodiscoverfrequentitemsetsandgenerateassociationrulesbasedontheseitemsets.However,theApriorialgorithmbecomescomputationallyexpensiveandtime-consumingwhenappliedtobigdataduetoitsinherentdownfalls,includingmultipledatabasescansandcandidategeneration. 2.2HadoopFramework Hadoopisanopen-sourceframeworkdesignedfordistributedstorageandprocessingofbigdata.Itenablesparallelprocessingoflargedatasetsacrossaclusterofcommodityhardware,providingfault-toleranceandscalability.HadoopconsistsoftheHadoopDistributedFileSystem(HDFS)andtheMapReduceprogrammingmodel. 3.ProposedApproach ToimprovetheefficiencyoftheApriorialgorithminabigdataenvironment,weproposeamodificationleveragingtheHadoopframework.Thekeyideaistoparallelizethe