Interpretations of Association Rules by Granular Computing

Authors:
Yuefeng Li;Ning Zhong
Affiliations:
-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

Citing 5
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Abstract

This paper presents interpretations for associationrules. It first introduces Pawlak's method, and thecorresponding algorithm of finding decision rules (a kindof association rules). It then uses extended random sets topresent a new algorithm of finding interesting rules. Itproves that the new algorithm is faster than Pawlak'salgorithm. The extended random sets are easily to includemore than one criterion for determining interesting rules.They also provide two measures for dealing withuncertainties in association rules.