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频繁项集挖掘算法研究 被引量:3

Research on Frequent Itemsets Mining Algorithm
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摘要 频繁项集挖掘是许多数据挖掘任务中的关键问题,也是关联规则挖掘算法的核心,所以提高频繁项集的生成效率一直是近几年数据挖掘领域研究的热点之一。本文以频繁项集挖掘算法的搜索方式和计数方式为主线,分析频繁项集挖掘中的代表性算法及其中的关键技术和方法,对近年来相关研究的新进展做了介绍和评述,并指出了未来的研究方向。 Mining the frequent itemset is a key problem of data mining, it is also the core of the algorithm for mining association rules. Therefore,to improve the efficiency of discovering the frequent itemsets is the issue in data mining area. In this paper, making the search methods and counting ways of the frequent intemsets mining algorithms as the main line, analyses the key technologies and methods of the representative frequent itemsets mining algorithms, and introduces the new developments of the research in this area, and disscusses the future direction of the research.
作者 蓝祺花 吴博
出处 《计算机与现代化》 2009年第3期60-65,共6页 Computer and Modernization
关键词 频繁项集 关联规则 研究 frequent itemsets association rules research
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参考文献18

  • 1Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases [ C ]//Proceedings of the ACM SIGMOD Conference on Management of Data. Washington D. C. ,1993:207-216.
  • 2Agrawal R, Srikant R. Fast algorithms for mining association rules [ C ]//Proceedings 20th Inter-national Conference on Very Large Data Bases. MorganKaufmann, 1994: 487 -499.
  • 3Hipp J, Guntzer U, Nakhaeizadeh G. Algorithms for association rule mining: A general survey and comparison[ J ]. SIGKDD Explorations, 2000, 2( 1 ) :58-64.
  • 4Savasere 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. Zurich Switzerland, 1995.
  • 5Zaki M J. Fast vertical Mining Using Diffsets[ R]. Technical Report 01-1, Rensselaer Polytechnic Institute, Troy, New York, 2001.
  • 6Han 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.
  • 7Park J S, Chen M S, Yu P S. An effective hash-based algorithm for mining association rules [ C ]//Proceedings of ACM SIGMOD International Conference on Management of Data. San Jose, CA, 1995:175-186.
  • 8Toivonen H. Sampling large databases for association rules [ C ]//Proceedings of the 22nd International Conference on Very Large Database. Bombay, India, 1996.
  • 9Brin S, Motwani R, U11man J D, et al. Dynamic itemset counting and implication rules for market basket data [C]//Preoceedings of the 1997 ACM SIGMOD International Conference on Management of Data. volume 26 (2) of SIGMOD Record ,ACM Press,1997 : 255-264.
  • 10Zaki M J, Parthasarathy S, Ogihara M, et al. New algorithms for fast discovery of association rules [ C ]//Proc. of the 3rd Int'l Conf. on KDD and Data Mining( KDD' 97). Newport Beach, California, 1997.

二级参考文献8

  • 1[1]R Agrawal,R Srikant.Fast algorithms for mining association rules.In:J Bocca,M Jarke,C Zaniolo,eds.Proc of the 20th Int'l Conf on Very Large DataBases (VLDB'94).San Francisco:Morgan Kaufmann,1994.487-499
  • 2[2]M Zaki,S Parthasarathy,M Ogihara,et al.New algorithms for fast discovery of association rules.In:D Heckerman,et al,eds.Proc of the 3rd Int'l Conf on Knowledge Discovery and Data Mining (KDD'97).Menlo Park,CA:AAAI Press,1997
  • 3[3]J Han,J Pei,Y Yin.Mining frequent patterns without candidate generation.In:M Dunham,J Naughton,W Chen,eds.Proc of 2000 ACM-SIGMOD Int'l Conf on Management of Data (SIGMOD'00).New York:ACM Press,2000.1-12
  • 4[5]G Grahne,J Zhu.Efficiently using prefix-trees in mining frequent itemsets.First Workshop on Frequent Itemset Mining Implementation (FIMI'03),Melbourne,FL,2003
  • 5[6]http://fuzzy.cs.uni-magdeburg.de/~borgelt/
  • 6[7]http://www.cs.helsinki.fi/u/goethals/
  • 7[8]http://www.ics.uci.edu/~mlearn/MLRepository.html
  • 8范明,李川.在FP-树中挖掘频繁模式而不生成条件FP-树[J].计算机研究与发展,2003,40(8):1216-1222. 被引量:56

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