Density peak clustering(DPC) can identify cluster centers quickly, without any prior knowledge. It is supposed that the cluster centers have a high density and large distance. However, some real datasets have a hierarchical structure, which will result in local cluster centers having a high density but a smaller distance. DPC is a flat clustering algorithm that searches for cluster centers globally, without considering local differences. To address this issue, a Multi-granularity DPC(MG-DPC) algorithm based on Variational mode decomposition(VMD) is proposed. MG-DPC can find global cluster centers in the coarse-grained space, as well as local cluster centers in the fine-grained space.In addition, the density is difficult to calculate when the dataset has a high dimension.Neighborhood preserving embedding(NPE) algorithm can maintain the neighborhood relationship between samples while reducing the dimensionality. Moreover, DPC requires human experience in selecting cluster centers. This paper proposes a method for automatically selecting cluster centers based on Chebyshev’s inequality. MG-DPC is implemented on the dataset of load-data to realize load classification. The clustering performance is evaluated using five validity indices compared with four typical clustering methods. The experimental results demonstrate that MG-DPC outperforms other comparison methods.
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