Abstract
In this paper, a fuzzy association rule mining approach with type-2 membership functions is proposed for dealing with data uncertainty. It first transfers quantitative values in transactions into type-2 fuzzy values. Then, according to a predefined split number of points, they are reduced to type-1 fuzzy values. At last, the fuzzy association rules are derived by using these fuzzy values. Experiments on a simulated dataset were made to show the effectiveness of the proposed approach.
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Chen, CH., Hong, TP., Li, Y. (2015). Fuzzy Association Rule Mining with Type-2 Membership Functions. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9012. Springer, Cham. https://doi.org/10.1007/978-3-319-15705-4_13
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DOI: https://doi.org/10.1007/978-3-319-15705-4_13
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