Abstract: Aiming at the problem that the density peak clustering (DPC) algorithm requires manually selected parameters (cutoff distance d_c),as well as the problem of a poor performance on complex data sets caused by the simple definition of local density and the one-step assignment strategy,a new density peak clustering algorithm based on natural nearest neighbors (NNN-DPC) is proposed.The algorithm does not need to specify any parameters and is a non-parametric clustering method.Based on the definition of natural nearest neighbors,this algorithm firstly gives a new local density calculation formula to describe the distribution of data,and reveals the internal connection.A two-step assignment strategy is designed to divide the sample points.Finally,the similarity between clusters is defined,and a new cluster merging rule is proposed to merge the clusters to obtain the final clustering result.The experimental results show that without parameters,the NNN-DPC algorithm has excellent generalization ability on various types of data sets,and can more accurately identify the number and distribution of clusters on manifold data or data with large differences of density between clusters,and assign sample points to the corresponding clusters.Compared with the perfor-mance indicators of DPC,FKNN-DPC,and three other classic clustering algorithms,the NNN-DPC algorithm has a great advantage.

Key words: Clustering algorithm, Density peaks, Local density, Natural nearest neighbor