计算机应用研究2023，Vol.40Issue(12)：3559-3565,7.DOI:10.19734/j.issn.1001-3695.2023.04.0162
二阶自然最近邻和多簇合并的密度峰值聚类算法
Second-order natural nearest neighbors and multi-clusters merge density peaks clustering algorithm
摘要
Abstract
The DPC algorithm identifies cluster centers based on local density and relative distance,ignoring the influence of the sample environment on the sample point density,so it is not easy to find cluster centers in low-density areas.The single-step allocation strategy of the DPC algorithm has poor fault tolerance,and once a sample point allocation error occurs,it will lead to a series of sample point allocation errors in the follow-up.To solve the above problems,this paper proposed a density peak clus-tering algorithm(TNMM-DPC)based on second-order natural nearest neighbor and multi-cluster merging.Firstly,it introduced the concept of second-order natural neighbor and considered the density of the sample point and the environment of the sample point at the same time,it redefined the local density of the sample point to reduce the influence of cluster density on the selec-tion of cluster center.Secondly,it defined the core point set to select the initial micro clusters,and allocated the sample points according to the correlation degree between the sample points and the micro clusters.Finally,it introduced the concept of neighbor boundary point set to merge the adjacent subclusters to obtain the final clustering results,avoiding the cascade effect of allocation errors.This paper compared TNMM-DPC algorithm with DPC and its improved algorithm on the artificial dataset and the UCI dataset,and the experimental results show that the TNMM-DPC algorithm can solve the problems existing in the DPC algorithm and can effectively cluster the artificial dataset and UCI dataset.关键词
密度峰值/自然邻居/局部密度/核心点集/子簇合并Key words
peak density/natural neighbors/local density/core point set/micro-cluster merging分类
信息技术与安全科学引用本文复制引用
张紫丹,徐华,杨重阳..二阶自然最近邻和多簇合并的密度峰值聚类算法[J].计算机应用研究,2023,40(12):3559-3565,7.基金项目
国家自然基金青年基金资助项目(62106088) （62106088）
