Interval Type-2 Relative Entropy Fuzzy C-Means clustering

Fuzzy set theory especially Type-2 fuzzy set theory provides an efficient tool for handling uncertainties and vagueness in real world observations. Among various clustering techniques, Type-2 fuzzy clustering methods are the most effective methods in the case of having no prior knowledge about observations. While uncertainties in Type-2 fuzzy clustering parameters are investigated by researchers, uncertainties associated with membership degrees are not very well discussed in the literature. In this paper, investigating the latter uncertainties is our concern and Interval Type-2 Relative Entropy Fuzzy C-Means (IT2 REFCM) clustering method is proposed. The computational complexity of the proposed method is discussed and its performance is examined using several experiments. The obtained results show that the proposed method has a very good ability in detecting noises and assignment of suitable membership degrees to observations.