南京大学学报（自然科学版）2026，Vol.62Issue(4)：592-606,15.DOI:10.13232/j.cnki.jnju.2026.04.007
基于相互近邻和证据理论的密度峰值聚类算法
Density peaks clustering algorithm based on mutual nearest neighbors and Dempster-Shafer theory
摘要
Abstract
Density Peaks Clustering(DPC)is a density-based clustering algorithm capable of automatically identifying clusters of arbitrary shapes without requiring the number of clusters to be pre-specified.However,when processing datasets containing clusters with significant density variations,DPC tends to erroneously select multiple cluster centers within dense clusters while overlooking the true centers in sparse clusters.Furthermore,DPC's single-step chained allocation strategy is prone to a"domino effect",where the misallocation of a single data point can trigger a cascade of erroneous assignments for subsequent points.To address these issues,this paper proposes a novel Density Peaks Clustering algorithm based on Mutual Nearest Neighbors and Dempster-Shafer Theory,termed MDS-DPC.First,by integrating a sample's distance-based similarity with its neighborhood cohesion,we redefine the calculation of local density to effectively mitigate inter-cluster density disparities.Second,to more accurately characterize the relative positional relationships between samples,we refine the relative distance metric by leveraging both local and global distribution characteristics,specifically through the integration of local density peaks and a mutual nearest neighbor graph.Finally,we introduce Dempster-Shafer theory and employ a multi-stage assignment strategy coupled with a hierarchical fine-tuning mechanism for cross-cluster connected samples to enhance the overall accuracy of sample allocation.We evaluated the proposed MDS-DPC algorithm against six state-of-the-art clustering algorithms on nine synthetic and twelve real-world datasets.The experimental results demonstrate that MDS-DPC achieves superior clustering performance.关键词
密度峰值聚类/相互近邻/局部密度峰/证据理论/多阶段分配Key words
density peaks clustering/mutual nearest neighbors/local density peaks/Dempster-Shafer theory/multi-stage assignment分类
信息技术与安全科学引用本文复制引用
张巳杨,张清华,周新然,邓偲,程云龙..基于相互近邻和证据理论的密度峰值聚类算法[J].南京大学学报（自然科学版）,2026,62(4):592-606,15.基金项目
国家重点研发计划(2026YFE0201300),国家自然科学基金(62576056),重庆市自然科学基金创新发展联合基金(CSTB2023-NSCQ-LZX0164),重庆市教委科学技术研究(KJZD-K202300613) （2026YFE0201300）
