Learning membership functions for fuzzy sets through modified support vector clustering

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.