Clustering with K-Means Hybridization Ant Colony Optimization (K-ACO)

One of well-known techniques in data mining is clustering. Clustering method which is very popular is K-means cluster because its algorithm is very easy and simple. However, K-means cluster has some weaknesses, one of which is that the cluster result is sensitive towards centroid initialization so that the cluster result tends to local optimal. This paper explains the modification of K-means cluster, that is, K-means hybridization with ant colony optimization (K-ACO). Ant Colony Optimization (ACO) is optimization algorithm based on ant colony behavior. Through K-ACO, the weaknesses of cluster result which tends to local optimal can be overcome well. The application of hybrid method of K-ACO with the use of R program gives better accuracy compared to K-means cluster. K-means cluster accuracy yielded by Minitab, Mathlab, and SAS at iris data is 89%. Meanwhile, K-ACO hybrid clustering with R program simulated on 38 treatments with 3-time repetitions gives accuracy result of 93,10%.