Mining positive and negative association rules from large databases

被引：0
|
作者：
Cornelis, Chris [1 ]
Yan, Peng
Zhang, Xing
Chen, Guoqing
机构：
[1] Univ Ghent, Dept Appl Math & Comp Sci, Ghent, Belgium
[2] Tsinghua Univ, Sch Econ & Management, Beijing 100084, Peoples R China
来源：
2006 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2006年
关键词：
association rules; data mining; Apriori;
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：
This paper is concerned with discovering positive and negative association rules, a problem which has been addressed by various authors from different angles, but for which no fully satisfactory solution has yet been proposed. We catalogue and critically examine the existing definitions and approaches, and we present an Apriori-based algorithm that is able to find all valid positive and negative association rules in a support-confidence framework. Efficiency is guaranteed by exploiting an upward closure property that holds for the support of negative association rules under our definition of validity.
引用
收藏
页码：152 / 157
页数：6
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