DPC algorithm is a new density based clustering algorithm that can automatically determine the number of clusters and cluster centers． However， there is a defect in the stability of clustering quality in the sample allocation strategy． KNN-DPC，an improved algorithm of DPC，has better clustering effect，but its practicality is affected by the low efficiency． In order to overcome the deficiencies of DPC algorithm and KNN-DPC algorithm，a neighbor density distribution optimized DPC clustering algorithm was proposed． Firstly， the algorithm searched and found the cluster centers with DPC algorithm． Then， two sample allocation strategies were adopted based on the neighbor density distribution of the sample，which was in turn used to assign the rest samples to the corresponding cluster． Theoretical analysis and the thorough experiments on several popular test cases include synthetic datasets and real-world datasets from UCI machine learning repository show that the clustering algorithm proposed can quickly determine the cluster center of arbitrary shape data and effectively perform sample cluster allocation． Compared with DPC algorithm and KNN-DPC algorithm， the proposed algorithm has a better balance between clustering effect and time performance，and has high stability． The algorithm proposed is an effective adaptive clustering algorithm that can be applied to largescale data sets．