The traditional clustering algorithms cluster the dataset repeatedly, and have poor computational efficiency on large datasets. In order to solve the problem, a novel algorithm based on hierarchy partition was proposed to determine the optimal number of clusters and initial centers of clusters, named Clusters Optimization based on Density of Hierarchical Division (CODHD). Based on hierarchical division, the computational process was studied, which did not need to cluster datasets repeatedly. First of all, all statistical values of clustering features were obtained by scanning dataset. Secondly, the data partitions of different level were generated from bottom-to-up, the density of each partition data point was calculated, and the maximum density point of each partition was taken as the initial center. At the same time, the minimum distance from the center to the higher density data point was calculated, the average of products' sum of the density of the center and the minimum distance was taken as the validity index and a clustering quality curve of different hierarchical division was built incrementally. Finally, the optimal number of clusters and the initial center of clusters were estimated corresponding to the partition of extreme points of curve. The experimental results demonstrate that, compared with Clusters Optimization on Preprocessing Stage (COPS), the proposed CODHD improved clustering accuracy by 30% and clustering algorithm efficiency at least 14.24%. The proposed algorithm has strong feasibility and practicability.