基于加权核密度估计与微簇合并的密度峰值聚类算法OA北大核心CSTPCD

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.