Abstract:In the present clustering method, k-means with potential function is the most commonly used algorithm, although the two algorithms have many advantages, but they also have their own limitations. The clustering number of k-means clustering algorithm cannot be determined, estimate in advance, at the same time sensitive to initial clustering center, and easy to be interfered by abnormal point, the clustering range of potential function clustering algorithm is limited, low efficiency of clustering multidimensional data. In view of the above two algorithms disadvantage, an improved clustering algorithm based on K-means and potential function is proposed in the paper. First, potential function method is used to determine the clustering number and initial center, and then cluster by using K-means method. The improved algorithm has the advantage of blind characteristics of potential function algorithm and also has the advantages of high efficiency of K-means. The experiment verified the validity of the improved algorithm, the results show that the improved algorithm have greatly improved in clustering accuracy and convergence speed.