In order to overcome the limitation of clustering by fast search and find of density peaks（ CFSFDP）which cannot automatically determine cluster centers and need human-centered interception of cluster centers through decision maps

an adaptive density peak clustering algorithm for cluster centers is proposed. Firstly

the distance matrix of data is calculated by using the method of Mahalanobis distance calculation. Secondly

according to the distribution rule of cluster centers

an adaptive cluster center extraction mechanism is proposed. Finally

the outlier resolution is adapted based on the description of cluster centers

so as to obtain the final clustering effect. In order to verify the effectiveness of the proposed method

experiments are carried out on a wide range of datasets.The results show that the proposed algorithm has better clustering results than the original CFSFDP algorithm on both manual test datasets and UCI datasets.