信息与控制2024，Vol.53Issue(3)：302-314,13.DOI:10.13976/j.cnki.xk.2024.3013
基于加权核密度估计与微簇合并的密度峰值聚类算法
Density Peaks Clustering Algorithm Based on Weighted Kernel Density Estimation and Micro-cluster Merging
摘要
Abstract
The density peaks clustering(DPC)algorithm is a widely used density-based clustering algo-rithm because of its simplicity and efficiency.However,although the DPC algorithm can easily di-vide a high-density cluster into multiple clusters,it is very easy to generate assignment linkage errors.In this regard,we propose a DPC algorithm based on weighted kernel density estimation and microcluster merging(WEMCM-DPC)that redefines the local density using kernel density es-timation and weighted K-nearest neighbors and reduces high-density clusters.The local density difference of sparse clusters improves cluster center identification.A new similarity measure be-tween microclusters is proposed that can reduce the influence of too sparse or dense samples in data on other samples,provide a basis for the merging of microclusters and improving the allocation error of the DPC algorithm,and improve accuracy of the clustering results.The WEMCM-DPC algorithm has been found to outperform the DPC and the four improved algorithms in clustering performance,as demonstrated by experimental data on datasets with uneven density distributions and real datasets.关键词
密度峰值/聚类/核密度估计/K近邻/微簇合并Key words
density peaks/clustering/kernel density estimation/K-nearest neighbor/micro-cluster merging分类
信息技术与安全科学引用本文复制引用
李智冈,吕莉,谭德坤,康平,樊棠怀..基于加权核密度估计与微簇合并的密度峰值聚类算法[J].信息与控制,2024,53(3):302-314,13.基金项目
国家自然科学基金项目(62066030) （62066030）
江西省教育厅科技项目(GJJ201915,GJJ220803) （GJJ201915,GJJ220803）
江西省重点研发计划项目(20192BBE50076,20203BBGL73225) （20192BBE50076,20203BBGL73225）
