The K-means clustering algorithm based on density and ant colony

The ant algorithm is a new evolutional method, k-means and the density-cluster are familiar cluster analysis. In this paper, we proposed a new K-means algorithm based on density and ant theory, which resolved the problem of local minimal by the random of ants and handled the initial parameter sensitivity of k-means. In addition it combined idea of density and made the ants searching selectable. With the experiments it was proved that the algorithm we proposed improved the efficiency and precision of cluster.