<p>Density peaks clustering (DPC) algorithm determines the cluster centers by calculating the local density and relative distance, and then assigns the non-center points to the cluster where the nearest high-density point is located. In this paper, an improved density peaks clustering algorithm based on mutual nearest neighbor distance (MD-DPC) is proposed to solve the problem of uncertainty and influence of the cut-off distance and poor fault tolerance of one-step allocation strategy. Firstly, the local density is calculated by the mutual nearest neighbor distance without cut-off distance to overcome the influence of cut-off distance parameter on the clustering results. Secondly, a measurement criterion is defined to consider the density of the region where the data points are located. According to this criterion, the data points are divided into core region and non-core region, and a two-step allocation strategies is proposed to overcome the problem of poor fault tolerance of the one-step allocation strategy of DPC. The allocation strategy of the data points in the core region is the same as the original DPC algorithm, and the data points in the non-core region are assigned to the cluster with the highest likelihood. Finally, in order to validate MD-DPC, we test it on synthetic and real-world datasets, and compare it with DPC, DBSCAN, k-means, KNN-DPC and DPCSA methods. Experimental results suggest that MD-DPC can effectively find clusters.</p>