陕西师范大学学报（自然科学版）2024，Vol.52Issue(3)：47-62,16.DOI:10.15983/j.cnki.jsnu.2024009
局部标准差优化的密度峰值聚类算法
Density peak clustering algorithm optimized with local standard deviation
摘要
Abstract
DPC(clustering by fast search and find of density peaks)algorithm is a density based clustering algorithm.It is one of the milestone clustering algorithms.It can find any arbitrary shapes of clusters embedded within any dimensional spaces.However,its local density definition of a point is not appropriate for simultaneously detecting the cluster centers of dense and sparse clusters,nor detecting the sparse and dense clusters subsequently.In addition,its one-step assignment strategy leads to a fatal problem,that is,once a point is assigned to an incorrect cluster,there are more subsequent points being assigned erroneously,resulting in the domino effect.To address the aforementioned problems,this paper redefines the local density of a point based on the local standard deviation,and proposes a two-step assignment strategy,resulting in the ESDTS-DPC algorithm.The ESDTS-DPC algorithm is compared with the original DPC and its variations including KNN-DPC,FKNN-DPC,DPC-CE and the classic density based clustering algorithm,such as DBSCAN.The extensive experiment results demonstrate superiority of the proposed ESDTS-DPC in detecting the clustering within a dataset.关键词
密度峰值聚类/标准差/局部密度/分配策略/聚类Key words
density peak clustering/standard deviation/local density/assignment strategy/clustering分类
信息技术与安全科学引用本文复制引用
谢娟英,张文杰..局部标准差优化的密度峰值聚类算法[J].陕西师范大学学报（自然科学版）,2024,52(3):47-62,16.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(GK202105003) （GK202105003）
