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New Technology of Library and Information Service  2008, Vol. 24 Issue (8): 63-69    DOI: 10.11925/infotech.1003-3513.2008.08.11
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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.

Key wordsFrequent item sets      Association rule mining      Data mining      Topic association     
Received: 09 May 2008      Published: 25 August 2008
: 

TP311 

 
  TP181

 
Corresponding Authors: Wang Qiang     E-mail: wq971120@163.com
About author:: Wang Qiang

Cite this article:

Wang Qiang. Algorithm for Mining Association Rule Based on the Identifier Lists of Transactions. New Technology of Library and Information Service, 2008, 24(8): 63-69.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2008.08.11     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2008/V24/I8/63

[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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