Abstract
In this paper, the fuzzy c-means (FCM) clustering approach is adopted to find concept drift of fuzzy membership functions. The proposed algorithm is divided into two stages. In the first stage, the FCM approach is used to find appropriate fuzzy membership functions at different periods or at different places. Then in the second stage, the proposed algorithm compares the results in the first stage to find different types of drift of fuzzy membership functions. Experiments are also made to show the performance of the proposed approach.
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Hong, T. P., Wu, M. T., Li, Y. K., & Chen, C. H. (2016). Mining drift of fuzzy membership functions. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9622, pp. 211–218). Springer Verlag. https://doi.org/10.1007/978-3-662-49390-8_20
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