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Algorithm for Mining Association Rule Based on the Identifier Lists of Transactions |
Wang Qiang1,2 |
1(National Science Library, Chinese Academy of Sciences, Beijing 100190, China)
2(Graduate University of the Chinese Academy of Sciences, Beijing 100049, China) |
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Abstract This paper designs and implements an algorithm named TidlistApriori for mining association rule based on the identifier lists of transactions in database using Java.The results of experiment comparing TidlistApriori with Apriori based on Hash-Tree indicate that this algorithm can improve the efficiency of finding frequent item sets, and TidlistApriori can be used as efficient tool for mining topic association.
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Received: 09 May 2008
Published: 25 August 2008
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Corresponding Authors:
Wang Qiang
E-mail: wq971120@163.com
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About author:: Wang Qiang |
[1] 毕建欣, 张岐山.关联规则挖掘算法综述[J].中国工程科学,2005,7(4):88-93.
[2] Jiawei H, Micheline K.数据挖掘概念和技术[M].范明,孟小峰译.北京:机械工业出版社,2001.
[3] Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules[C]. In:Proc of the 21th International Conference on Very Large Database. Chile,1994:487-499.
[4] Savasere A, Omiecinski E, Navathe S. An Efficient Algorithm for Mining Association Rules in Large Databases[C]. In:Proc of the 21th International Conference on Very Large Database. Switzerland, 1995:432-443.
[5] Park J S, Chen M S, Yu P S. An Effective Hash-based Algorithm for Mining Association Rules[C].In:Proceedings of the 1995 ACM SIGMOD International Conference on Management of data.ACM,1995:175-186.
[6] 李淑芝,郑剑. 一种基于Hash-tree的产生关联规则的方法[J]. 南昌大学学报:理科版),2004,28(2):197-204.
[7] Mannila H, Toivonen H, Verkamo A. Efficient Algorithm for Discovering Association Rules[C]. AAAIWorkshop on Knowledge Discovery in Databases.1994:181-192.
[8] Brin S, Motwani R, Ullman J D, Tsur S. Dynamic Itemset Counting and Implication Rules for Market Basket Analysis[J]. ACM SIGMOD Record, 1997,26(2):255-264. |
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