Interval type-2 fuzzy C-means using multiple kernels

In this paper, we propose an adaptive hybrid clustering method, where fuzzy C-means with multiple kernels (FCM-MK) has been combined with interval type-2 fuzzy C-means. In the proposed method, multiple Gaussian kernels are used. The resolution-specific weight, the membership values, and the cluster prototypes are decided in situ. In the calculation of the cluster prototypes, uncertainty associated with the fuzzifier parameter m is considered. In doing so, a pattern set is extended to interval type-2 fuzzy sets using two fuzzifiers m₁ and m₂, creating a footprint of uncertainty (FOU) for the fuzzifier m. This is followed by type reduction and defuzzification for obtaining the final location of the prototypes. Various experimental results are shown to validate the effectiveness of the proposed clustering method.