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基于垂直二进制位图的频繁模式挖掘算法 被引量:2

Algorithm of mining frequent patterns based on the vertical bitmap
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摘要 采用垂直二进制位图映射事务数据库,提出了用二进制位图生成一种新的NBFP-Tree结构,并据此提出了一种新的频繁模式挖掘算法NBFP-mine.该算法不产生候选集,对NBFP-Tree结构进行深度优先遍历一次,就可从NBFP-Tree结构上直接查找出最大频繁模式.最后,从理论分析和实践验证了它的高效性. The vertical bitmap transaction database is introduced to propose a new data structure of the NBFP-Tree based on it. A new algorithm, NBFP-mine, is also offered which is used to mine maximal frequent patterns . This method does not generate any Candidate, which can query the maximal fi'equent patterns easily from the NBFP-Tree directly by once accessing depth-first ergod- ie of this data structure. Finally, its high efficiency is proved theoretically and experimentally.
出处 《山东大学学报(理学版)》 CAS CSCD 北大核心 2007年第5期24-29,共6页 Journal of Shandong University(Natural Science)
基金 北京市教委科技发展计划资助项目(KM200510016002) 北京建筑工程学院青年基金资助项目(1004034)
关键词 垂直二进制位图 二进制位串 NBFP-Tree结构 NBFP-mine算法 vertical bitmap bit vector NBFP-Tree structure NBFP-mine algorithm
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参考文献10

  • 1Han Jia-wei,Pei Jian,Yin Yi-wen.Mining frequent patterns without candidate generation[A].Proceedings of the 2000 ACM-SIGMOD International Conference on Management of Data[C].Dallas,Texas,US:ACM Press,2000.1-12.
  • 2Burdick D,Calimlim M,Gehrke J.Mafia:A maximal frequent itemset algorithm for transactional databases[A].Proceedings of the 17th International Conference on Data Engineering[C].Heidelberg,Germany:IEEE Press,2001.443-452.
  • 3陈耿,朱玉全,杨鹤标,陆介平,宋余庆,孙志挥.关联规则挖掘中若干关键技术的研究[J].计算机研究与发展,2005,42(10):1785-1789. 被引量:62
  • 4Guanling Lee,K L Lee,Arbee L P Chen.Efficient graph-based algorithms for discovering and maintaining association rules in large databases[J].Knowledge and Information Systems,2001,3(3):338-355.
  • 5郑玲霞,李大学,马万里.基于有向图的关联规则算法[J].重庆邮电学院学报(自然科学版),2005,17(4):495-498. 被引量:5
  • 6刘博,郑启伦,彭宏.基于关联图的频集快速发现算法[J].计算机工程与设计,2006,27(17):3136-3139. 被引量:1
  • 7黄龙军,章志明,段隆振,黄明和.一种基于无向项集图的频繁项集挖掘算法[J].计算机工程与应用,2006,42(16):177-179. 被引量:3
  • 8Roberto J,Bayardo Jr.Efficiently mining long patterns from databases[A].Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data[C].Seattle,Washington,US:ACM Press,1998.85-93.
  • 9Carter C L,Hamilton H J.Efficient attribute-oriented algorithms for knowledge discovery from large databases[J].IEEE Transactions on Knowledge and Data Engineering,1998,10(2):193-208.
  • 10Robert J Hilderman,Howard J Hamilton,Nick Cercone.Data mining in large databases using domain generalization graphs[J].Intelligent Information Systems,1999,13(3):195-234.

二级参考文献34

  • 1R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large databases. ACM SIGMOD Int'l Conf. Management of Data, Washington, D. C., 1993.
  • 2Han J, Kamber. MData Mining: Concepts and Techniques.Beijing: High Education Press, 2001.
  • 3B. Goethals. Survey of frequent pattern mining. Helsinki Institute for Information Technology, Technical Report, 2003.
  • 4R. Agrawal, R. Srikant. Fast algorithm for mining association rules. The 20th Int'l Conf. VLDB, Santiago, Chile, 1994.
  • 5M. Houtsma, A. Swami. Set-oriented mining for association rules in relational databases. In: Yu P., Chen A, eds. Proc. Int'l Conf. Data Engineering. Los Alamitos, CA: IEEE Computer Society Press, 1995. 25~33.
  • 6A. Savasere, E. Omiecinski, S. Navathe. An efficient algorithm for mining association rules. The 21st Int' l Conf. VLDB, Zurich,Switzerland, 1995.
  • 7J. Han, Y. Fu. Discovery of multiple-level association rules from large databases. The 21st Int'l Conf. VLDB, Zurich,Switzerland, 1995.
  • 8R. Bayardo. Efficiently mining long patterns from databases. In:L. M. Haas, A. Tiwary, eds. Proc. ACM SIGMOD Int'l Conf.Management of Data. New York: ACM Press, 1998. 85~93.
  • 9Lin, Dao-I, Z. M. Kedem. Pincer-Search: A new algorithm for discovering the maximum frequent set. In: H. J. Schek, F.Saltor, I. Ramos et al. eds. Proc. 6th European Conf.Extending Database Technology. Berlin: Springer-Veriag, 1998.105~119.
  • 10D.W. Cheung, J. Han, V. T. Ng, et al. Maintenance of discovered association rules in large databases: An incremental updating technique. The 12th Int'l Conf. Data Engineering, New Orleans, Louisiana, 1996.

共引文献66

同被引文献14

  • 1颜跃进,李舟军,陈火旺.基于FP-Tree有效挖掘最大频繁项集[J].软件学报,2005,16(2):215-222. 被引量:68
  • 2Bayardo R J. Efficiently mining long patterns from databases[ C]. Proc. of the ACM-SIGMOD Intl Conf. Management of Data ( SIGMOD98 ). Seattle, Washington : 1998,85 -93.
  • 3Agarwal R C,Aggarwal C C,Prasad V V V. Depth first generation of long patterns[ C]. In Proceedings of the ACM SIGMOD Conference ,2000.
  • 4Burdick D, Calimlim M, Gehrke J. MAFIA: a maximal frequent itemset algorithm for transactional databases[C]. Intl Conf. on Data Engineering,2001.
  • 5Zhou Q H,Weslcy C, Lu B J. SmaltMiner:a depth 1st algorithm guided by tail information for mining maximal frequent itcmsets [C]. In: Proc. of the IEEE Int 1 Conf. on Data Mining (ICDM2002) ,2002,570-577.
  • 6Grahne G, Zhu J. Efficiently using prefix-trees in mining frequent itemsets[ C]. In:l st Workshop on Frequent ltemset Mining Implementation( FIMI03 ) ,2003.
  • 7http://fimi, cs. hclsinki, fl/data/,2006.
  • 8朱玉全,宋余庆.频繁闭项目集挖掘算法研究[J].计算机研究与发展,2007,44(7):1177-1183. 被引量:10
  • 9马丽生,邓辉文,齐逸.基于FP-tree的最大频繁项目集挖掘算法[J].计算机工程与设计,2008,29(2):385-388. 被引量:4
  • 10李洪波,周莉,张吉赞.用垂直数据格式构建FP增长树的算法[J].计算机工程与应用,2009,45(8):161-164. 被引量:4

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