Strongest association rules mining for personalized recommendation

被引：0
|
作者：
Li, Jie [1 ,2 ]
Xu, Yong [2 ,3 ]
Wang, Yun-Feng [1 ]
Chu, Chao-Hsien [4 ]
机构：
[1] School of Management, Hebei University of Technology, Tianjin 300401, China
[2] School of Science, Hebei University of Technology, Tianjin 300401, China
[3] Institute of Systems Engineering, Tianjin University, Tianjin 300072, China
[4] School of Information Science and Technology, Pennsylvania State University, PA 16801, United States
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2009年 / 29卷 / 08期
关键词：
Association rules - Matrix algebra;
D O I：
10.1016/s1874-8651(10)60064-6
中图分类号：
学科分类号：
摘要：
The notion of strongest association rules (SAR) was proposed, a matrix-based algorithm was developed for mining SAR set. As the subset of the whole association rule set, SAR set includes much less rules with the special suitable form for personalized recommendation without information loss. With the SAR set mining algorithm, the transaction database is only scanned for once, the matrix scale becomes smaller and smaller, so that the mining efficiency is improved. Experiments with three data sets show that the number of rules in SAR set in average is only 26.2% of the total number of whole association rules, which mitigates the explosion of association rules.
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页码：144 / 152
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