An improved k-means clustering algorithm

K-means algorithm used in the initial cluster centers are randomly generated, then clustering results are unstable and susceptible to the noise data-objects. In this paper presents a density-based algorithm to determine the initial cluster centers, eliminate the clustering results depend on the initial cluster centers. While, optimized the methods of cluster centers re-calculation and the distance from data-object to the cluster center, reduce noise impact on the clustering results, which meets the clustering of asymmetry density cluster. Experiments on UCI datasets show that the improved algorithm can eliminate the clustering results depend on the initial cluster centers, obtain more compact cluster. Therefore, the improved K-means clustering algorithm is effective.