Abstract: Density peak clustering algorithm(DPC) is a simple and efficient unsupervised clustering algorithm.Although the algorithm can automatically discover cluster centers and realize efficient clustering of arbitrary shape data,it still has some defects.Aiming at the three defects of density peak clustering algorithm,which does not consider the location information of data when defining the correlation value,the number of clustering centers needs to be set manually in advance,and the chain reaction is easy to occur when distributing sample points,an adaptive density peak clustering algorithm based on shared nearest neighbor is proposed.Firstly,the shared nearest neighbor is used to redefine the local density and other measures,and the local characteristics of data distribution are fully considered,so that the spatial distribution characteristics of sample points can be better reflected.Se-condly,by introducing the phenomenon of density attenuation,the sample points are automatically gathered into micro-clusters,which realizes the adaptive determination of cluster number and the adaptive selection of cluster center.Finally,a two-stage distribution method is proposed,in which the micro-clusters are merged to form the backbone of the cluster,and then the backbone of the cluster allocated in the previous step guides the distribution of the remaining points,avoiding the occurrence of chain reactions.The implementation on two dimensional composite datasets and UCI datasets shows that this algorithm has better perfor-mance in most cases than the classical density peak clustering algorithm and its improved algorithms in recent years.

Key words: Shared nearest neighbor, Density peak clustering, Allocation strategy, Cluster center, Density decay