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Reverse-Nearest-Neighbor-Based Clustering by Fast Search and Find of density peaks

Chinese Journal of Electronics 2023年第6期32卷 1341-1354页

作者： ZHANG Chunhao XIE Bin ZHANG Yiran College of Computer and Cyber Security Hebei Normal University Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University

Clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.

关键词： density peaks Reverse nearest neighbor Clustering Cluster fusion

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

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Chinese Journal of Electronics 2021年第4期30卷 658-668页

作者： GU Ziwen LI Peng LANG Xun YU Yixuan SHEN Xin CAO Min School of Information Science and Engineering Yunnan University Electric Power Research Institute Yunnan Power Grid Co. Ltd.

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.

关键词： density peaks Variational mode decomposition Neighborhood preserving embedding Granular computing Shape clustering

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