Fuzzy clustering of fuzzy data considering the shape of the membership functions using a novel representation learning technique
Most existing distance measures for fuzzy data do not capture differences in the shapes of the left and right tails of membership functions. As a result, they may calculate a distance of zero between fuzzy data even when these differences exist. Additionally, some distance measures cannot compute distances between fuzzy data when their membership functions differ in type. In this paper, inspired by human visual perception, we propose a fuzzy clustering method for fuzzy data using a novel representation...