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.
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Zhou, Z., Wu, Z., Wang, C., Feng, Y. (2006). Efficiently Mining Both Association and Correlation Rules. In: Wang, L., Jiao, L., Shi, G., Li, X., Liu, J. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2006. Lecture Notes in Computer Science(), vol 4223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881599_42
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DOI: https://doi.org/10.1007/11881599_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45916-3
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