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一种基于OUS的最大频繁项集挖掘算法

Algorithm for mining maximal frequent itemsets based on OUS
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摘要 分析实际应用中有效访问序列的特点,提出了一种采用自底向上策略快速挖掘最大频繁项集的OUS算法。该算法首先对用户项集进行重叠操作统计浏览次数,然后合并,依据用户给出的最小支持度删除原项集中的非频繁页面元素,并对两两用户项集筛选生成候选频繁项集,最后扫描数据库,统计各个候选频繁项集的支持度计数。实验结果表明,该算法能有效地发现用户最大频繁项集。 The characteristics of effective access sequence in the actual application are analyzed and an efficient algorithm OUS based bottom-up strategy is proposed for mining maximal frequent itemsets.The algorithm first takes count of the browse number of each access sequence by overlapping operation,then unites and deletes the unfrequent page items according to minimum support degree given by users,afterwards sifts getting the intersections of each two user access pattern and gives birth to candidate grequent access patterns, at last, adds up the number of each candidate frequent access pattern by scanning the original database. Experimental results show that the OUS algorithm can discover user maximal frequent access patterns effectively.
出处 《计算机工程与应用》 CSCD 北大核心 2008年第24期148-150,共3页 Computer Engineering and Applications
基金 国家自然科学基金No.60773100 教育部科学技术研究重点项目No.205014 河北省教育厅科研计划项目No.2006143~~
关键词 有效访问序列 重叠 筛选 合并 最大频繁项集 effective access sequence overlap sift unite maximal frequent itemset
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  • 1徐章艳,刘美玲,张师超,卢景丽,区玉明.Apriori算法的三种优化方法[J].计算机工程与应用,2004,40(36):190-192. 被引量:71
  • 2Lin Dao I,Proc the 6th European Conference on Extending Database Technology,1998年,105页
  • 3Agrawal R,Proc the 11th Inter Conference on Data Engineering,1995年,3页
  • 4Agrawal R,Srikant R.Fast algorithms for mining association rules in large databases[C]//Proc 20th Int'l Conf Very Large Data Bases,Sept 1994,1994:478-499.
  • 5Han J,Pei J,Yin Y.Mining frequent patterns without candidate generation[C]//Proc 2000 ACM-SIGMOD Int Conf Management of Data (SIGMOD'00),Dalas,TX,May 2000.
  • 6Park J S,Chen Ming-Syan,Yu Philip S.Using a hash-based method with transaction trimming for mining association rules[J].IEEE Transactions on Knowledge and Data Engineering,1997,9(5).
  • 7Agrawal R,Srikant R.Fast algorithms for mining association rules Reearch Report RJ 9839[R].IBM Almaden Research Center,San Jose,CA,June 1994.
  • 8Savasere A,Omiecinski E,Navathe S.An efficient algorithm for mining association rules in large databases[C]//Proceedings of the 21st International Conference on Very large Database,1995.
  • 9Brin S,Motwani R,Ullman J D,et al.Dynamic Itemset counting and implication rules for market basket data[C]//ACM SIGMOD International Conference on the Management of Data,1997.
  • 10黄进,尹治本.关联规则挖掘的Apriori算法的改进[J].电子科技大学学报,2003,32(1):76-79. 被引量:51

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