Bacterial Colony Optimization for Data Clustering

Clustering is the process of the grouping object in a given data objects based on their similarity between data objects. The swarm intelligent based clustering algorithms are generally stochastic search techniques that followed by the morality of cooperative behavior and self organization of an insect group. On the other hand, data clustering may be well planned as a complicated global optimization problem. Hence, the bacterial colony optimization is a new optimization algorithm which is followed by the behaviors of bacteria and proposed in this paper for solving the data clustering problem. The experimental results show that the proposed model achieves better clustering efficiency compared with other conventional clustering methods.