The initial clustering centers of K-means algorithm are randomly selected, which may result in low accuracy and unstable clustering. To solve these problems, a K-means initial clustering center selection algorithm was proposed. The locations of data points were determined by analyzing Difference of K-dist (DK) graph. One point with the least k-dist value on the main density curves was selected as an initial clustering center. The experimental results demonstrate that the improved algorithm can select unique initial clustering center, gain stable clustering result, get higher accuracy and reduce times of iteration.