COOPERATIVE SWARM INTELLIGENCE BASED EVOLUTIONARY APPROACH TO FIND OPTIMAL CLUSTER CENTER IN CLUSTER ANALYSIS
Bighnaraj Naik, Sarita Mahapatra · 2012
The Centroid-based clustering is an NP-hard optimization problem and the common approach is to search for cluster centers only for approximate solutions. In this paper we have proposed swarm intelligence based nature-inspired center-based clustering method using PSO optimization. Proposed PSO clustering method is capable to search best cluster with maximum fitness using social-only and cognition-only model, such that the square distances from the cluster are minimized. In this article, how PSO based clustering can be used to get N number of cluster specified by the user in a dataset is demonstrated. Our suggested method has been tested with artificial dataset and several datasets from UCI Machine learning repository. Effectiveness and usefulness of the proposed method is shown by comparing fitness of this method with Kmeans and Fuzzy c-means technique. For better comparative result, we have ended up with comparison of proposed clustering model with subtractive clustering (extension of the mountain clustering) to ensure proposed method computes optimal number of cluster in a dataset. Results shows that, this method is quite simple, effective and has much potential to search best cluster centers in multidimensional search space.