Density Peaks Clustering Algorithm Based on Weighted Kernel Density Estimation and Micro-cluster Merging

The density peaks clustering (DPC) algorithm is a widely used density-based clustering algorithm because of its simplicity and efficiency. However, although the DPC algorithm can easily divide 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 estimation 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 between 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.