Fuzzy clustering using linguistic-valued exponent

TL;DR

This paper introduces a novel fuzzy c-means clustering algorithm that incorporates hedge algebra theory to model the exponent parameter, enhancing its ability to handle linguistic-valued variables in practical clustering tasks.

Contribution

It proposes a new FCM-based algorithm utilizing hedge algebra to better model the exponent parameter for linguistic-valued variables.

Findings

The new algorithm effectively solves clustering problems in practice.

Experimental results demonstrate improved clustering performance.

The method successfully integrates hedge algebra into FCM.

Abstract

The purpose of this paper is to study the algorithm FCM and some of its famous innovations, analyse and discover the method of applying hedge algebra theory that uses algebra to represent linguistic-valued variables, to FCM. Then, this paper will propose a new FCM-based algorithm which uses hedge algebra to model FCM's exponent parameter. Finally, the design, analysis and implementation of the new algorithm as well some experimental results will be presented to prove our algorithm's capacity of solving clustering problems in practice.