A brief introduction to the concepts in fuzzy clustering followed by a discussion on fuzzy c-means algorithm and its limitations are presented in section

In Data mining, Fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms in dealing with the challenges posed by large collections of vague and uncertain natural data. This paper reviews concept of fuzzy logic and fuzzy clustering. The classical fuzzy c-means algorithm is presented and its limitations are highlighted. Based on the study of the fuzzy c-means algorithm and its extensions, we propose a modification to the cmeans algorithm to overcome the limitations of it in calculating the new cluster centers and in finding the membership values with natural data. The efficiency of the new modified method is demonstrated on real data collected for Bhutan’s Gross National Happiness (GNH) program. Keywords—Adaptive fuzzy clustering, clustering, fuzzy logic, fuzzy clustering, c-means.