Abstract
We propose an algorithm for inferring membership functions of fuzzy sets by exploiting a procedure originated in the realm of support vector clustering. The available data set consists of points associated with a quantitative evaluation of their membership degree to a fuzzy set. The data are clustered in order to form a core gathering all points definitely belonging to the set. This core is subsequently refined into a membership function. The method is analyzed and applied to several real-world data sets.
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Malchiodi, D., Pedrycz, W. (2013). Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering. In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_6
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DOI: https://doi.org/10.1007/978-3-319-03200-9_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03199-6
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