Evolution of Fuzzy Clustering

Abstract

Fuzzy clustering has been discussed in order to reflect the pervasiveness of imprecision and uncertainty which exists in the real world. The amount of data is growing and we are faced with a challenge to analyze, process and extract useful information from the complicated vast amount data. Many fuzzy clustering methods have been developed in order to deal with such data. This paper provides outlines of some fuzzy clustering methods in which the target data is asymmetric similarity data or interval-valued data. Moreover, a method for the interpretation of the fuzzy clustering result is stated.