A New Interestingness Measure of Association Rules

Authors:
Jianhua Liu;Xiaoping Fan;Zhihua Qu
Affiliations:
-;-;-
Venue:
WGEC '08 Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing
Year:
2008

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Abstract

Discovering association rules is one of the most important tasks in data mining. The classical model of association rules mining is support-confidence, the interestingness measure of which is the confidence measure. The classical Interestingness measure in Association Rules have existed some disadvantage. In this paper, some problem of interestingness measures on the classical association rules model have been analyzed, and then a new interestingness measure for mining association rules is proposed based on sufficiency measure of uncertain reasoning to improve the classical method of mining association rules. The property of the new interestingness measures is analyzed. Its validity, has been tested in this paper.