A Multi-Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition

• 作者: GU Ziwen LI Peng LANG Xun YU Yixuan SHEN Xin CAO Min
• 来源期刊: 电子学报（英文版）
• 年，卷（期）: 2021,（4）
• 所属期刊栏目: COMPUTERS AND MICROELECTRONICS
• 页码范围: 658-668
• 页数: 11页
• 语种: 英文
• DOI: 10.1049/cje.2021.03.001

摘要

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.