An association rules algorithm based on kendall-τ

Authors:
Anping Zeng;Yongping Huang
Affiliations:
School of Computer and Information Engineering, Yibin University, Yibin, Sichuan, China;Computational Physics Key Laboratory of Sichuan Province, Yibin University, Yibin, Sichuan, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

The disadvantages of apriori algorithm are firstly discussed. Then, a new measure of kendall-τ is proposed and treated as an interest threshold. Furthermore, an improved Apriori algorithm called K-apriori is proposed based on kendall-τ correlation coefficient. It not only can accurately find the relations between different products in transaction databases and reduce the useless rules but also can generate synchronous positive rules, contrary positive rules and negative rules. Experiment has been carried out to verify the effectiveness of the algorithm. The result shows that the algorithm is effective at discovering the association rules in a sales management system.