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1、一种改进的Apriori关联规则挖掘算法张广路1,雷景生2,吴兴惠1(1.海南师范大学数学与统计学院,海南海口571158;2.南京邮电大学信息与技术学院,江苏南京211815)摘要:关联规则挖掘是数据挖掘中的一个重要研究内容。为了高效、快速地从事务数据库中挖掘出频繁项集,针对数据挖掘的经典关联规则Apriori算法的瓶颈问题提出了改进的方法。算法将事物数据库映射到布尔型数组中,然后所有的操作都针对数组元素值展开。这样大大减少了数据库的扫描次数。算法利用数组的随机访问特性及布尔型数据的简单“与”操作,直接产生频繁项集,而不产生大量的候选项集。经理
2、论分析和实验结果显示该算法在效率上明显优于Apriori算法。关键词:数据挖掘;关联规则;Apriori算法;频繁项集中图分类号:TP311文献标识码:A文章编号:1673-629X(2010)06-0084-05AnImprovedAprioriAlgorithmforMiningAssociationRulesZHANGGuang2lu1,LEIJing2sheng2,WUXing2hui1(1.SchoolofMathematicsandStatistics,HainanNormalUniversity,Haikou571158,China
3、;2.SchoolofInformationScienceandTechnology,NanjingUniversityofPostsandTelecommunications,Nanjing211815,China)Abstract:Associationruleminingisanimportantpartofresearchcontentindatamining.Inordertoefficientlyandquicklymineallfre2quentitemsetfromthetransactiondatabase,animproved
4、algorithmofminingassociationrulesispresentedforthebottleneckproblemoftheclassicApriorialgorithm.ThetransactiondatabaseismappedtoBoolarray,thenalltheoperationsarecarriedoutbasedonarrayele2mentsvalue,therebyreducingthedatabasescanningfrequency.Thenusebitwise“AND”operationandran
5、domaccesscharacteristicsofarray,adirectconsequenceoffrequentitemsets,ratherthanhavealargenumberofcandidatesets,therebyimprovingtheefficiencyofthealgorithm.Keywords:datamining;associationrules;Apriorialgorithm;frequentitemsetpredeterminedminimumsupportcount,min-sup.Sec2ond,gen
6、eratinginterestingassociationrulesfromthefre2quentitemsets,i.e.,theserulesmustsatisfyminimumsupportandminimumconfidence.Becausethesecondstepismuchlesscostlythanthefirststep,theoverallperfor2manceofminingassociationrulesisdeterminedbythefirststep,sominingassociationrulesisusua
7、llyconvertedtominingfrequentitemsets.Nowadays,therehavemanyworks1～5focusonfrequentitemsetsmining.Frequentpatternminingoftengeneratesaverylargenumberofpat2ternsandrules,whichreducesnotonlytheefficiencybut0IntroductionMiningAssociationRulesisaresearchfieldbypro2posedearlier,asa
8、nimportantpartofdatamining,thathasexperiencedthedevelopmentofalonger
