Clustering as an unsupervised learning technique has been widely used in practice. In this paper, a novel clustering algorithm based on region segmentation (CRS) is proposed. It aims to automatically evolve the optimal number of clusters as well as the clusters of the data sets based on the data density. First, a new data density is given based on the reverse near neighbor enhancement which can make the clusters detection more effectively. Then, the multiple sub-region centers can be determined through the data density. Moreover, a merge criterion is proposed to make the relevant regions be merged and obtain the final clustering results. The proposed algorithm does not need to know the number of clusters in advance and no threshold limit. Therefore, it can be used more widely. In the experiments, we compare the performance of our CRS algorithm with DBSCAN, IS-DBSCAN, STClu, DP, and SCDOT algorithms on synthetic, and real-world data sets. Experimental results demonstrated that the NMI, ACC, F1 and ARI obtained by CRS algorithm is always better than that obtained by the other algorithms for the same data sets.