Density Peaks Clustering Based on Weighted Local Density Sequence and Nearest Neighbor Assignment

Density peaks clustering (DPC) is a density-based clustering algorithm with excellent clustering performance including accuracy, automatically detecting the number of clusters, and identifying center points. However, the local density of DPC strongly depends on the cutoff distance which must be prespecified; in addition, the strategy assigns each remaining point to the same cluster as its nearest neighbor of higher density in descending order of local density, which is likely to cause cluster label error propagation. To overcome these limitations, we propose an improved DPC by introducing weighted local density sequence and two-stage assignment strategies, called DPCSA. Many previous improved DPC algorithms neglect additional complexity, whereas DPCSA incorporates the nearest neighbor dynamic table to enhance clustering efficiency. The experimental results for 12 artificial and 11 real-world datasets, including Olivetti face, verify that the DPCSA clustering performance is significantly superior to DPC and DPC via heat diffusion (HDDPC), and slightly superior to fuzzy weighted k-nearest neighbors density peak clustering (FKNNDPC). In addition, the DPCSA is more computationally efficient than FKNNDPC and HDDPC, but less than DPC. The source code of DPCSA is available at https://www.github.com/Yu123456/DPCSA.