An optimal rough fuzzy clustering algorithm using particle swarm optimisation

Rough fuzzy hybrid models are widely used for handling uncertain and vague data and are very efficient in handling real life applications. Particle swarm optimisation (PSO) has been found to be a useful tool to optimise and find the best out of a set of solutions. In this paper, we propose a computational algorithm by embedding PSO in rough fuzzy hybrid clustering, which forms overlapping clusters with optimised partition. The proposed algorithm uses rough fuzzy C-means to formulate fuzzy lower and fuzzy boundary region of the clusters based on membership of objects with respect to their prototypes. This method has been applied to a swarm of clusters to get the best partitions at local and global levels qualified by Davies Bouldin (DB) and Dunn (D) indexes as fitness measures. This algorithm generates clusters dynamically and its superiority over other existing clustering techniques is established experimentally by taking several real world datasets.