Data clustering using particle swarm optimization and bee algorithm

Abstract

Clustering is the process of organising data into meaningful groups, and these groups are called clusters. It is a way of grouping data samples together that is similar in some way, according to some criteria that you pick. Swarm intelligence (SI) is a collective behavior of social systems like insects such as ants (ant colony optimization, ACO), fish schooling, honey bees (bee algorithm, BA) and birds (particle swarm optimization, PSO). In this paper, a hybrid Swarm Intelligence based technique for data clustering is proposed using Particle Swarm Optimization and Bee Algorithm. Recent studies have shown that hybridization of K-means and PSO are more suitable for clustering large data sets. As the k-means algorithm tends to converge faster than PSO algorithm but usually trapped in a local optimal area. A new way of integrating BA with PSO proposed in this paper.