Efficiently mining both association and correlation rules

Authors:
Zhongmei Zhou;Zhaohui Wu;Chunshan Wang;Yi Feng
Affiliations:
College of Computer Science and Technology, Zhejiang University, China;College of Computer Science and Technology, Zhejiang University, China;College of Computer Science and Technology, Zhejiang University, China;College of Computer Science and Technology, Zhejiang University, China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

Associated and correlated patterns cannot fully reflect association and correlation relationships between items like both association and correlation rules. Moreover, both association and correlation rule mining can find such type of rules, “the conditional probability that a customer purchasing A is likely to also purchase B is not only greater than the given threshold, but also significantly greater than the probability that a customer purchases only B. In other words, the sale of A can increase the likelihood of the sale of B.” Therefore, in this paper, we combine association with correlation in the mining process to discover both association and correlation rules. A new notion of a both association and correlation rule is given and an algorithm is developed for discovering all both association and correlation rules. Our experimental results show that the mining combined association with correlation is quite a good approach to discovering both association and correlation rules.