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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CITATION STYLE
Chen, C. H., Hong, T. P., & Li, Y. (2015). Fuzzy association rule mining with type-2 membership functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9012, pp. 128–134). Springer Verlag. https://doi.org/10.1007/978-3-319-15705-4_13
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