A Graph Adaptive Density Peaks Clustering algorithm for automatic centroid selection and effective aggregation
作者：
Highlights：
• A novel GA-DPC is proposed based on the DPC algorithm and Graph Theory.
• Centroids can be detected automatically instead of selected manually in DPC.
• A new aggregation principle is proposed to eliminate the domino effect in DPC.
• Outliers and edge points can be detected easily in GA-DPC.
• The experimental results demonstrate the above advantages of the proposed GA-DPC.
摘要
•A novel GA-DPC is proposed based on the DPC algorithm and Graph Theory.•Centroids can be detected automatically instead of selected manually in DPC.•A new aggregation principle is proposed to eliminate the domino effect in DPC.•Outliers and edge points can be detected easily in GA-DPC.•The experimental results demonstrate the above advantages of the proposed GA-DPC.
论文关键词：Density Peaks Clustering algorithm (DPC),GA-DPC,Graph Theory,Centroid selection
论文评审过程：Received 25 April 2021, Revised 9 November 2021, Accepted 9 January 2022, Available online 29 January 2022, Version of Record 14 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116539
