Discovering interesting association rules by clustering

被引：0
|
作者：
Zhao, YC [1 ]
Zhang, CQ [1 ]
Zhang, SC [1 ]
机构：
[1] Univ Technol Sydney, Fac Informat Technol, Sydney, NSW 2007, Australia
来源：
AI 2004: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2004年 / 3339卷
关键词：
interestingness; association rules; clustering;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
There are a great many metrics available for measuring the interestingness of rules. In this paper, we design a distinct approach for identifying association rules that maximizes the interestingness in an applied context. More specifically, the interestingness of association rules is defined as the dissimilarity between corresponding clusters. In addition, the interestingness assists in filtering out those rules that may be uninteresting in applications. Experiments show the effectiveness of our algorithm.
引用
收藏
页码：1055 / 1061
页数：7
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