Fuzzy K-means using non-linear S-Distance

Abstract

A considerable amount of research has been done since long to select an appropriate similarity or dissimilarity measure in cluster analysis for exposing the natural grouping in an input dataset. Still, it is an open problem. In recent years, the research community is focusing on divergence-based non-Euclidean similarity measure in partitional clustering for grouping. In this paper, the Euclidean distance of traditional Fuzzy k-means (FKM) algorithm is replaced by the S-distance, which is derived from the newly introduced S-divergence. Few imperative properties of S-distance and modified FKM are presented in this study. The performance of the proposed FKM is compared with the conventional FKM with Euclidean distance and its variants with the help of several synthetic and real-world datasets. This study focuses on how the proposed clustering algorithm performs on the adopted datasets empirically. The comparative study illustrates that the obtained results are convincing. Moreover, the achieved results denote that the modified FKM outperforms some state-of-the-art FKM algorithms.