基于相互近邻和证据理论的密度峰值聚类算法OA

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.