An improved firefly fuzzy C-means (FAFCM) algorithm for clustering real world data sets

Abstract

Fuzzy c-means has been widely used in clustering many real world datasets used for decision making process. But sometimes Fuzzy c-means (FCM) algorithm generally gets trapped in the local optima and is highly sensitive to initialization. Firefly algorithm (FA) is a well known, popular metaheuristic algorithm that simulates through the flashing characteristics of fireflies and can be used to resolve the shortcomings of Fuzzy c-means algorithm. In this paper, first a firefly based fuzzy c-means clustering and then an improved firefly based fuzzy c-means algorithm (FAFCM) has been proposed and their performance are being compared with fuzzy c-means and PSO algorithm. The experimental results divulge that the proposed improved FAFCM method performs better and quite effective for clustering real world datasets than FAFCM, FCM and PSO, as it avoids to stuck in local optima and leads to faster convergence. © Springer International Publishing Switzerland 2014.