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关联规则发现的一种改进算法 被引量：9

An Improved Algorithm for Mining Association Rules
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摘要 在Apriori算法基础上 ,给出一个改进的关联规则发现算法·由于这个算法只需对交易数据库进行一次搜索 ,能大量减少所需的I/O次数 ,且内存开销适中 ,因此同其他关联规则发现算法相比具有快速的优点 ,适合于大型交易数据库·使用合成数据作试验表明这个算法尤其对大型数据库的性能优于先前已有的一些关联规则算法· Based on the Apriori Algorithm, an improved algorithm for the discovery of association rules in large database was presented. The method only needs one pass over the database,and reduces I/O overheads greatly. Its memory usage is moderate, so this algorithm is especially suitable for large databases. Experiments with synthetic database were made.The algorithm is better than some previous algorithms for very large databases.
作者 张晓辉 何耀东 万家华 赵宏
机构地区 东北大学软件中心
出处 《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2001年第4期401-404,共4页 Journal of Northeastern University(Natural Science)
基金 国家'八六三'高技术计划资助项目 ( 863 30 6 ZD0 2 0 2 6)
关键词 数据挖掘 关联规则 交易数据库 频繁项集 算法 data mining association rule transaction database large itemsets
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献7

  • 1[1]Fayyad U M,Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery:An overview[M]. Fayysd UM,Piatetsky-Shapiro G. Advances in Knowledge Discovery and Data Mining.1-35.
  • 2[2]Brachman R J,Fnand T. The process of knowledge discovery in d atabases:a human-centered approach[M]. Fayysd UM,Piatetsky-Shapiro G. Advanc es in Knowledge Discovery and Data Mining,37-58.
  • 3[3]Agrawal R,Imielinski T,Swami A. Mining association rules b etween sets of items in large database[A]. Proceedings of ACM SIGOD Conference on Management of Data[C]. Washington DC,1993.207-216.
  • 4[4]Agrawal R,Srikant R.Fast algorithms for mining association ru les in large databases[A]. Proceedings of the 20th International Conferenc e on Very Large Databases[C]. Santiago,Chile,1994.
  • 5[5]Houtsma M,Swami A.Set-oriented mining of association rules[ R]. Research Report RJ 9567. San Jose:IBM Almaden Research Center,1993.
  • 6[6]Strikant R,Agrawal R. Mining quantitative association rul es in large relational tables[A]. Proceedings of ACM SIGMOD Conference on Mana gement of Data(SIGMOD'96)[C]. Montreal,1996.1-12.
  • 7[7]Strikant R,Agrawal R.Mining generalized association rules[A ]. Proceedings of the 21st International Conference on Very Large Databases[C ]. Zurich,1995.407-419.

同被引文献33

  • 1郑英,王继成,韩海冰.数据挖掘在电信业务精确营销中的应用[J].网络安全技术与应用,2008(10):33-34. 被引量：8
  • 2李清峰,杨路明,张晓峰,龙艳军.数据挖掘中关联规则的一种高效Apriori算法[J].计算机应用与软件,2004,21(12):84-86. 被引量：29
  • 3Jiawei Han, Micheline Kamber. Concepts and Techniques [ M ].China Machine Press,2001,152-161.
  • 4R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large database [ C ]. In Proceedings of ACM_SIGMOD Conference on Management of Data, Washington, D. C. , May 1993:207-216.
  • 5R. Agrawal, R. Srikant. Fast algorithms for mining association rules[ C]. In Proceedings of the 20th International Conference on Very Large Database, Santiago, Chile. September 1994,487- 499.
  • 6R. Agrawal, J. Shafer. Parallel mining of association rules:Design, Implementation and Experience [ C ]. IEEE Trans.Knowledge and Data Engineering, 1996,962-969.
  • 7Ch. Cherif Latiri, BenYahia S. Generating implicit association rules from textual data[C]. Proceedings of the ACS/IEEE International Conference on Computer Systems and Applications,2001. 137-143.
  • 8Liu B, Hsu W, Ma Y. Integrating classification and Association rule mining [C]. Proc of the int confon knowledge discovery and data mining[C]. New York:AAAI Press, 1998.80-86.
  • 9Li Wen-min, Han Jia-wei, Pei Jian. CMAR:Aaccurate and efficient classification based on multiple class association rules [C].California:Morgan Kaufmann, 2001. 369-376.
  • 10John D Holt, Soon M Chung. Mining association rules in text databased using multipass with inverted hashing and pruning[C].USA:IEEE Computer Society, 2002. 49-56.

引证文献9

二级引证文献42

东北大学学报（自然科学版）
2001年 第4期

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