Scalable swarm based fuzzy clustering

Iterative fuzzy clustering algorithms are sensitive to initialization. Swarm based clustering algorithms are able to do a broader search for the best extrema. A swarm inspired clustering approach which searches in fuzzy cluster centroids space is discussed. An evaluation function based on fuzzy cluster validity was used. A swarm based clustering algorithm can be computationally intensive and a data distributed approach to clustering is shown to be effective. It is shown that the swarm based clustering results in excellent data partitions. Further, it is shown that the use of a cluster validity metric as the evaluation function enables the discovery of the number of clusters in the data in an automated way.