REFINEMENT OF CLUSTERS FROM K-MEANS WITH ANT COLONY OPTIMIZATION

Clustering is a distribution of data into groups of similar objects. In this paper, Ant Colony Optimization (ACO) is proposed to improve k-means clustering. Though the k-means is one of the best clustering algorithm, the quality is based on the starting condition and it may converge to local minima. And an important point is, so far, the researchers have not contributed to improve the cluster quality after grouping. Our proposed method has two phases: in the first step, the initial seeds for k-means clustering are selected based on statistical modes to converge to a “better ” local minimum. And in the second step, we have proposed a novel method to improve the cluster quality by ant based refinement algorithm. The proposed algorithm is tested in medical domain and shows that refined initial starting points and post processing refinement of clusters indeed lead to improved solutions