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

GU Ziwen; LI Peng; LANG Xun; YU Yixuan; SHEN Xin; CAO Min

doi:10.1049/cje.2021.03.001

Volume 30 Issue 4

Jul. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(4): 658-668

GU Ziwen, LI Peng, LANG Xun, et al., “A Multi-Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 658-668, 2021, doi: 10.1049/cje.2021.03.001

Citation:

GU Ziwen, LI Peng, LANG Xun, et al., “A Multi-Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition,” Chinese Journal of Electronics, vol. 30, no. 4, pp. 658-668, 2021, doi: 10.1049/cje.2021.03.001

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

doi: 10.1049/cje.2021.03.001

GU Ziwen¹,
LI Peng^{1
,
,},
LANG Xun¹,
YU Yixuan¹,
SHEN Xin²,
CAO Min²

1. School of Information Science and Engineering, Yunnan University, Kunming 650500, China;
2. Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming 650217, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.61763049) and the Key Project of Applied Basic Research of Yunnan Province (No.2018FA032).

Received Date: 2020-09-28
Available Online: 2021-07-19
Publish Date: 2021-07-05

Abstract

Abstract

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.
- Density peaks,
- Variational mode decomposition,
- Neighborhood preserving embedding,
- Granular computing,
- Shape clustering

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References(30)

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