安庆师范大学学报（自然科学版）2024，Vol.30Issue(2)：41-46,6.DOI:10.13757/j.cnki.cn34-1328/n.2024.02.007
基于相互K近邻的密度峰值聚类算法
Density Peaks Clustering Algorithm Based on Mutual K-Nearest Neighbor
摘要
Abstract
Density peaks clustering, a kind of clustering algorithm with simple principle and high efficiency, faces several challenges, such as disunity in density definition, easy error in cluster centers selection and"domino"phenomenon in sample allocation. To solve these problems, a density peaks clustering algorithm based on mutual K-nearest neighbor (MKDPC) is pro-posed. Firstly, an improved density is defined based on the mutual K-nearest neighbor of samples, which unifies the density definition method of DPC algorithm, and can effectively avoid the problem of cluster centers selection error of variable densi-ty datasets. Secondly, the shared mutual K-nearest neighbor and similarity between samples are defined based on mutual K-nearest neighbor, and then a multi-step sample allocation strategy is proposed, which can effectively overcome the"domino"phenomenon in the process of sample allocation. Experiments are carried out on synthetic datasets and real datasets, and the MKDPC algorithm is compared with other four alternative methods, with results substantiating its efficacy.关键词
密度峰值聚类/相互K近邻/局部密度/分配策略Key words
density peaks clustering/mutual K-nearest neighbor/local density/allocation strategy分类
信息技术与安全科学引用本文复制引用
赵志忠,陈素根..基于相互K近邻的密度峰值聚类算法[J].安庆师范大学学报（自然科学版）,2024,30(2):41-46,6.基金项目
国家自然科学基金项目(61702012),安徽省自然科学基金项目(2008085MF193)和安徽省高校自然科学研究重点项目(2022AH051053) （61702012）
