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基于一次性数据库访问策略的关联规则挖掘算法的研究 被引量:2

The Research on the Mining Association Rules Algorithm Based on the Strategy Accessing to Database Once
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摘要 针对大型数据库挖掘中需要多次访问数据库和效率较低的局限性,提出一次性数据库访问策略,设计了基于Apriori的Apriori_ADO算法.经过实验分析,Apriori_ADO算法降低了算法的时间和空间开销,提高了关联规则挖掘效率.算法具有很强的实用性,已用于超市中顾客消费知识的挖掘,并取得了满意的结果. This article analyzes the Apriori algorithm,and points out the limitations that it requires many accesses in the large-scale database mining and its efficiency is low.Based on the strategy accessing to DB once,it designs Apriori_ADO algorithm based on the Apriori.This algorithm avoids cutting frequently.Experimental analysis shows that the new improved Apriori_ADO algorithm reduces the time and space,and improves the mining efficiency of association rules.Apriori_ADO algorithm has strong practicality;it has been used for supermarkets in customer consumer knowledge mining,and has achieved satisfying results.
出处 《微电子学与计算机》 CSCD 北大核心 2010年第12期22-25,共4页 Microelectronics & Computer
基金 国家自然科学基金项目(60673170) 陕西省教育厅自然科学基金专项(08JK318) 西安建筑科技大学校人才基金项目(RC0618)
关键词 关联规则 APRIORI算法 频繁项集 支持度 association rules Apriori algorithm frequent itemsets support
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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
  • 2陈凯,冯全源.最大频繁项集的高效挖掘[J].微电子学与计算机,2005,22(8):22-25. 被引量:13
  • 3Houts ma M, Swami A. Set-oriented Mining of Association Rules [R]. Research Report RJ 9567. San Jose: IBM Almaden Research Center, 1993.
  • 4Agrawal R, I mielinski T, Swami A. Mining Association Rules between Sets of Items in Large Database [A]. Proceedings of ACM SIGOD Conference on Management of Data[C]. Washinton DC, 1993:207-216.
  • 5Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Database [A]. Proceeding of the 20th International Conference on Very Large Databases [C].Santiago, Chile, 1994.
  • 6Xie Jun, Xie Kanglin. An Improved Algorithm for Mining Association Rules.
  • 7Chengqi Zhang,Shichao Zhang.Association rule mining model and algorithms[J].Springer I,2002, 2307:33-39.
  • 8Wu Xindong,Chengqi Zhang,Shichao Zhang.Mining both positive and negative association rules[C].Proceedings of 19th International Conference on Machine Learning,Sydney:Australia,2002.658-665.
  • 9GI Webb.Efficient search for association rules proc[C].ACM SIGKDD Int'l Conf.Knowledge Discovery and Data Mining,2000:99~107.
  • 10Zhang C,Zhang S.Collecting quality data for database mining[C].Proceedings of the 14th Australian Joint Conference on Artificial Intelligence,2001,593-556.

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