重庆大学学报2026，Vol.49Issue(8)：53-65,13.DOI:10.11835/j.issn.1000-582X.2026.08.006
基于邻域扩展的自适应密度聚类算法
Adaptive density clustering algorithm based on neighborhood expansion
摘要
Abstract
Density-based clustering can effectively identify clusters with different shapes and densities and has therefore attracted extensive attention in the field of data mining.However,efficient estimation of local density and accurate identification of density peaks remain key challenges for existing density clustering methods.To address these issues,this paper proposes an adaptive density clustering algorithm based on neighborhood expansion.First,the dynamic reverse nearest neighbor sequence of each data object is calculated.Second,dynamic information entropy is derived from the reverse nearest neighbor sequence and used to identify density centers.Finally,adaptive clustering is achieved by iteratively expanding neighborhood and dynamically determining local density centers.Experimental results show that the proposed algorithm can accurately identify density centers and adaptively discover clusters with various shapes and densities.In addition,it shows strong robustness to noise and variable-density data.Compared with DBSCAN,DPC,and RNN-DBSCAN,the proposed algorithm achieves superior clustering performance.关键词
密度中心/自适应聚类/动态信息熵/动态逆近邻序列Key words
density center/adaptive clustering/dynamic information entropy/dynamic reverse nearest neighbor sequence分类
交通工程引用本文复制引用
卢建云,邵俊明,黄伟,胡刚林..基于邻域扩展的自适应密度聚类算法[J].重庆大学学报,2026,49(8):53-65,13.基金项目
国家自然科学基金资助项目(62376054).Supported by the National Natural Science Foundation of China(62376054). （62376054）
