Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering
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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.
KeywordsMembership Function Membership Grade Information Granule Obese Class Machine Learn Research
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