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Mining of Multi-dimensional Association Rules over Interval Data using Clustering and Characterization  

Lim, Seung-Hwan (한양대학교 전자컴퓨터통신공학과)
Kwon, Yong-Suk (삼성전자 무선연구소)
Kim, Sang-Wook (한양대학교 전자컴퓨터통신공학과)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.16, no.1, 2010 , pp. 60-64 More about this Journal
Abstract
To discover association rules from nontransactional data, there have been many studies on discretization of attribute values. These studies do not reflect the change of discovered rules' confidence according to the change of the ranges of the discretized attributes, and perform the discretization stage and the rule discovery stage independently. This causes the ranges of attributes not properly discretized, thereby making the rules having high confidence excluded in the result set. To solve this problem, we propose a novel method that performs the discretization and rule discovery stages simultaneously in order to discretize ranges of attributes in such a way that the rules having high confidence are discovered well. To the end, we perform hierarchical clustering on the attributes in the right hand side of rules, then do characterization on every cluster thus obtained. The experimental result demonstrates that our method discovers the rules having high confidence better than existing methods.
Keywords
Association Rules; Data Mining; Clustering; Characterization;
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