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快速更新频繁闭合项目集算法 被引量:1

Fast Updating Algorithm of Frequent Closed Itemsets
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摘要 频繁闭合项目集集可惟一确定频繁项目集完全集且数量小得多,然而有关频繁闭合项目集的更新还不多见。为此,提出快速更新频繁闭合项目集算法—FUAFCI(Fast Updating Algorithm of Frequent Closed Itemsets),该算法主要考虑最小支持度发生变化时频繁闭合项目集的更新情况。FUAFCI在最坏的情况下仅须扫描各局部数据库一遍,且利用CLOSET+的项目集合并、子项目集修剪以及子集检验等优化策略及已挖掘的结果,可确保对频繁闭合项目集进行高效的更新。验结果表明,FUAFCI算法是有效可行的。 The set of frequent closed itemsets determines exactly the complete set of all frequent itemsets and is usually much smaller than the laster.Yet very little work has been done for updating of frequent closed itemsets.Therefore, in this paper,we introduce an fast updating algorithm of frequent closed itemsets-FUAFCI,which considers the updating of frequent closed itemsets when dynamically adjusting minimum support measure threshold.In worse case,FUAFCI only scans transaction database once,Moreover,using the previously mined frequent closed itemsets and the item merging,item skipping,sub-itemset pruning methods,and so on in CLOSET+,FUAFCI can obviously improves updating efficiency of frequent closed itemsets.Experimental results show that FUAFCI algorithm is efficient and effective.
出处 《计算机工程与应用》 CSCD 北大核心 2006年第36期148-151,共4页 Computer Engineering and Applications
基金 国家自然科学基金资助项目(70371015) 江苏省自然科学基金资助项目(BK2005135) 江苏省高校自然科学研究项目基金资助项目(05KJB520066)
关键词 数据挖掘 频繁闭合项目集 更新 data mining frequent dosed itemsets updating
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参考文献13

  • 1Agrawal R,ImielinSki T,Swami A.Mining association rules between sets of items in large databases[C]//Proceedings of the ACM SIGMOD International Conference on Management of Data,Washington,DC,1993:207-216.
  • 2Agrawal R,Srikant R.Fast algorithms for mining association rules[C]//Proceedings of the 20th International Conference Very Large Data Bases (VLDB' 94),Santiago,Chile,1994:487-499.
  • 3Han J W,Pei J,Yin Y.Mining frequent patterns without candidate generation[C]//Proceedings of the 2000 ACM SIGMOD International Conference on management of data,Dallas,USA,2000:1-12.
  • 4杨明,孙志挥,杨萍.基于记录分区的加权关联规则挖掘[J].小型微型计算机系统,2003,24(10):1779-1782. 被引量:6
  • 5冯玉才,冯剑琳.关联规则的增量式更新算法[J].软件学报,1998,9(4):301-306. 被引量:227
  • 6杨明,孙志挥.一种基于前缀广义表的关联规则增量式更新算法[J].计算机学报,2003,26(10):1318-1325. 被引量:23
  • 7杨明,孙志挥,吉根林.快速挖掘全局频繁项目集[J].计算机研究与发展,2003,40(4):620-626. 被引量:35
  • 8杨明,孙志挥,宋余庆.快速更新全局频繁项目集[J].软件学报,2004,15(8):1189-1197. 被引量:18
  • 9Pasquier N,Bastide Y,Taouil R,et al.Discovering frequent closed itemsets for association rules[C]//Beeri C.Proc of the 7th Int'l Conf on Database Theory.Jerusalem:Springer-Verlag,1999:398-416.
  • 10Pei J,Han J W,Mao R.CLOSET:An efficient algorithm for mining frequent closed itemsets[C]//Gunopulos D.Proc of the 2000 ACM SIGMOD Int'l Workshop on Data Mining and Knowledge Discovery.Dallas:ACM Press,2000:21-30.

二级参考文献23

  • 1RAgrawa1 TImie1inSki Aswami.Mining association ru1es between sets of items in 1arge database[J].The ACM SIGMOD Intemationa1 Conf on Management of Data, Washington, DC,1993,.
  • 2[1]Pasquier N, Bastide Y, Taouil R, Lakhal L. Discovering frequent closed itemsets for association rules. In: Beeri C, et al, eds. Proc. of the 7th Int'l. Conf. on Database Theory. Jerusalem: Springer-Verlag, 1999. 398~416.
  • 3[2]Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Beeri C, et al, eds. Proc. of the 20th Int'l. Conf. on Very Large Databases. Santiago: Morgan Kaufmann Publishers, 1994. 487~499.
  • 4[3]Pei J, Han J, Mao R. CLOSET: An efficient algorithm for mining frequent closed itemsets. In: Gunopulos D, et al, eds. Proc. of the 2000 ACM SIGMOD Int'l. Workshop on Data Mining and Knowledge Discovery. Dallas: ACM Press, 2000. 21~30.
  • 5[4]Burdick D, Calimlim M, Gehrke J. MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Georgakopoulos D, et al, eds. Proc. of the 17th Int'l. Conf. on Data Engineering. Heidelberg: IEEE Press, 2001. 443~452.
  • 6[5]Zaki MJ, Hsiao CJ. CHARM: An efficient algorithm for closed itemset mining. In: Grossman R, et al, eds. Proc. of the 2nd SIAM Int'l. Conf. on Data Mining. Arlington: SIAM, 2002. 12~28.
  • 7[6]Liu JQ, Pan YH, Wang K, Han J. Mining frequent item sets by opportunistic projection. In: Hand D, et al, eds. Proc. of the 8th ACM SIGKDD Int'l. Conf. on Knowledge Discovery and Data Mining. Alberta: ACM Press, 2002. 229~238.
  • 8[7]Srikant R. Quest synthetic data generation code. San Jose: IBM Almaden Research Center, 1994. http://www.almaden.ibm.com/ software/quest/Resources/index.shtml
  • 9[8]Blake C, Merz C. UCI Repository of machine learning. Irvine: University of California, Department of Information and Computer Science, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html
  • 10Lin J L, Dunham M H. Mining association rules: anti-skew algorithms. IEEE Tran. on Knowledge Engineering, 1998 (2) : 486-493.

共引文献297

同被引文献20

  • 1徐章艳,刘作鹏,杨炳儒,宋威.一个复杂度为max(O(|C||U|),O(|C^2|U/C|))的快速属性约简算法[J].计算机学报,2006,29(3):391-399. 被引量:234
  • 2陈俊杰,崔晓红.基于FP-Tree的频繁闭合项目集挖掘算法的研究[J].计算机工程与应用,2006,42(34):169-171. 被引量:3
  • 3Agrawal R, Imielinski T, Swami A.Mining association rules between sets of items in large database[C]//Proc of 1993 ACM-SIGMOD International Conference on Management of Data, 1993:207-216.
  • 4Park J S,Chen M S,Yu P S.An effective Hash-based algorithm for mining association rules[C]//Proc of 1995 ACM-SIGMOD International Conference on Management of Data, 1995: 175-186.
  • 5Savasere A,Omiecinski E,Navathe S.An efficient algorithm for mining association rules in large databases[C]//Proc of the 21st International Conference on Very Large Databases, 1995:211-220.
  • 6Hart J W, Pei J, Yin Y.Mining frequent patterns without candidate generation[C]//Proc of 2000 ACM-SIGMOD International Conference on Management of Data,2000:1-12.
  • 7Pawlak Z.Rough sets[J].International Journal of Computer and Information Science, 1982,11 (5):341-356.
  • 8Wong S K M,Ziarko W.Optimal decision rules in decision table[J].Bulletin of Polish Academy of Sciences, 1985, 33: 693-696.
  • 9Skowron A, Rauszer C.The discernibility matrices and functions in information systems[C]//Slowinski R.Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory.Dordrecht: Kluwer Academic Publishers, 1991:331-362.
  • 10Hu X H, Cercone N.Leaming in relational databases: A rough set approach[J].International Journal of Computational Intelligence, 1995,11 (2) :323-338.

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