An improved density peaks clustering based on sparrow search algorithm

Density peaks clustering (DPC) algorithm has attracted the attention of scholars because of its simplicity and efficiency. However, it certainly has some disadvantages. On the one hand, the cut-off distance of DPC is artificially set, which greatly affects the clustering results of the DPC. On the other hand, the one-step allocation strategy is not robust and has poor fault tolerance. In this paper, we propose an improved density peaks clustering based on sparrow search algorithm(SSA-DPC) to solve the problems. First, the cut-off distance is optimized by the sparrow search algorithm with the ACC index as the object function to reduce the impact of cut-off distance on clustering results. Second, the idea of mutual nearest neighbor is introduced to divide the dataset into high-density region and low-density region, and different allocation strategies are adopted for different regions to overcome the problem of poor fault tolerance of one-step allocation strategy in DPC. Finally, in order to validate SSA-DPC, we test it on synthetic and real-world datasets, and compare it with DPC, SNN-DPC, DBSCAN, k-means, KNN-DPC and DPCSA methods. Experimental results suggest that SSA-DPC can effectively find clusters.