计算机科学与探索2025，Vol.19Issue(4)：929-944,16.DOI:10.3778/j.issn.1673-9418.2405064
加权共享近邻优化的密度峰值聚类算法
Density Peak Clustering Algorithm Optimized by Weighted Shared Neighbors
摘要
Abstract
DPC(clustering by fast search and find of density peaks)algorithm's local density definition varies with the size of a dataset,the local density of a point is sensitive to the cutoff distance dc,and its single-step assignment strategy for the remaining points can cause the"domino effect",resulting in its incapability in finding the genuine clustering in a dataset.To address the limitations,this paper proposes a density peak clustering algorithm based on weighted shared neighbors(WSN-DPC).This algorithm utilizes standard deviation weighted distance to enhance the Euclidean distance,thereby highlighting the contributions of different features to the distances between points.Additionally,shared neighbor information is used to define the similarities between points,and the local density and relative distance of a point are defined,so as to reflect the true distribution of points within a dataset as far as possible.Furthermore,distinct assignment strategies are employed in turn for outliers and non-outliers in the dataset,so as to guarantee that each point is to be assigned to its most appropriate cluster.Extensive experiments across multiple datasets and the statistically significant test demonstrate that the proposed WSN-DPC is superior to DPC and its variants,while addressing the limitations of DPC.关键词
共享近邻/局部密度/加权距离/类簇中心/聚类Key words
shared neighbor/local density/weighted distance/cluster center/clustering分类
计算机与自动化引用本文复制引用
张文杰,谢娟英..加权共享近邻优化的密度峰值聚类算法[J].计算机科学与探索,2025,19(4):929-944,16.基金项目
国家自然科学基金(62076159,61673251,12031010) （62076159,61673251,12031010）
中央高校基本科研业务费专项资金(GK202105003).This work was supported by the National Natural Science Foundation of China(62076159,61673251,12031010),and the Fundamental Research Funds for the Central Universities of China(GK202105003). （GK202105003）
