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