Density peaks clustering based on density backbone and fuzzy neighborhood
作者：
Highlights：
• A novel dynamic density peaks clustering method called DPC-DBFN is proposed.
• A fuzzy kernel is proposed to compute the local densities of the data points.
• A graph-based label propagation strategy is used to identify backbones, border areas and noisy points.
• DPC-DBFN can effectively assign true labels to border points located in overlapped regions.
• The results on real-world, images and synthetic data show the effectiveness of the proposed method.
摘要
•A novel dynamic density peaks clustering method called DPC-DBFN is proposed.•A fuzzy kernel is proposed to compute the local densities of the data points.•A graph-based label propagation strategy is used to identify backbones, border areas and noisy points.•DPC-DBFN can effectively assign true labels to border points located in overlapped regions.•The results on real-world, images and synthetic data show the effectiveness of the proposed method.
论文关键词：Fuzzy kernel,Density peaks clustering,Noise detection,Label propagation
论文评审过程：Received 14 June 2019, Revised 27 December 2019, Accepted 13 May 2020, Available online 24 May 2020, Version of Record 14 June 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107449
