Density peaks clustering based on k‐nearest neighbors sharing

• Published in: Concurrency and computation Vol. 33; no. 5
• Main Authors: Fan, Tanghuai, Yao, Zhanfeng, Han, Longzhe, Liu, Baohong, Lv, Li
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 10.03.2021; Wiley Subscription Services, Inc
• Subjects: Algorithms; Clustering; Datasets; Density; density peak clustering; Fault tolerance; k‐nearest neighbors; local density; natural neighbors; shared neighbors; Winding
• ISSN: 1532-0626; 1532-0634
• DOI: 10.1002/cpe.5993

Summary The density peaks clustering (DPC) algorithm is a density‐based clustering algorithm. Its density peak depends on the density‐distance model to determine it. The definition of local density for samples used in DPC algorithm only considers distance between samples, while the environments of samples are neglected. This leads to the result that DPC algorithm performs poorly on complex data sets with large difference in density, flow pattern or cross‐winding. In the meantime, the fault tolerance of allocation strategy for samples is relatively poor. Based on the findings, this article proposes a density peaks clustering based on k‐nearest neighbors sharing (DPC‐KNNS) algorithm, which uses the similarity between shared neighbors and natural neighbors to define the local density of samples and the allocation. Comparison between theoretical analysis and experiments on various synthetic and real data reveal that the algorithm proposed in this article can discover the cluster center of complex data sets with large difference in density, flow pattern or cross‐winding. It can also provide effective clustering.