A Novel Density Peaks Clustering based on Support Point and Nearest Neighbor Relationship

Density peaks clustering (DPC) algorithm is a robust density-based clustering method that is extensively used to analyze datasets characterized by irregular structures and noise. However, it still has some issues. On the one hand, the local density of DPC may cause centers in low-density clusters to be easily ignored. On the other hand, the allocation method of DPC often leads to a chain reaction if the samples are assigned incorrectly. To address above issues, a novel density peaks clustering algorithm based on support point and nearest neighbor relationship (DPC-SN) is proposed. First, the local density is redefined to mitigate the impact of density variations between any two clusters. Second, the concept of boundary degree is presented to detect the boundary points. Then, a multi-step assignment method is proposed to assign non-center points by introducing the idea of shared nearest neighbor, which avoids continuous clustering error. Finally, DPC-SN is compared and analyzed with various typical clustering algorithms on synthetic and real datasets. The experimental results verify the effectiveness of the DPC-SN.