Interpretations of Association Rules by Granular Computing

This paper presents interpretations for association rules. It first introduces Pawlak’s method, and the corresponding algorithm of finding decision rules (a kind of association rules). It then uses extended random sets to present a new algorithm of finding interesting rules. It proves that the new algorithm is faster than Pawlak’s algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. They also provide two measures for dealing with uncertainties in association rules.

• Type: Conference
• Year: 2003
• Where: ICDM
• Authors: Yuefeng Li, Ning Zhong
• Keywords: Association Rules, Data Mining, Extended Random Sets, ICDM 2003, Interesting Rules