Guided by the principle of three-way decision as thinking in threes, three-way clustering addresses the information uncertainty problem using the core region and the fringe region to characterize a cluster. The universe is split into three parts by these two regions, which capture three kinds of relationships between objects and a cluster, namely, belonging to, partially belonging to, and not belonging to. In this paper, a new three-way clustering algorithm is proposed based on the improved DPC (density peak clustering). Firstly, the natural nearest neighbor algorithm is introduced to adaptively obtain the number of neighbors of each point to define the local density and local of each sample. Secondly, density peaks is obtained by decision-making figure and selected as the clustering center. At last, a three-way assignment strategy is use to determine core region and fringe region of each cluster. Experimental results on UCI datasets and artificial datasets show that the proposed method can effectively improve the performances of clustering results.