Unsupervised hypergraph convolutional clustering networks

Mingwei You; Nan Xiang; Qilin Wang; Zhenguo Wang

doi:10.1117/12.3013388

29 November 2023 Unsupervised hypergraph convolutional clustering networks

Mingwei You, Nan Xiang, Qilin Wang, Zhenguo Wang

Author Affiliations +

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 1293716 (2023) https://doi.org/10.1117/12.3013388
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Deep clustering algorithms based on graph convolutional networks are widely used due to their strong ability to mine network structure. However, the construction of neighborhood graphs may introduce noise and affect the clustering results. Meanwhile, focusing on ordinary topology alone ignores the higher-order connections between data about attributes. To address the above problems, an unsupervised hypergraph convolutional clustering network (UHCCN) is proposed in this paper. We construct hypergraph structures using attributes and incorporate higher-order information encoding into representation learning through hypergraph convolution. Using an attribute encoder will extract node features and fuse it into the hypergraph convolution. Finally, representation learning and clustering are optimized jointly. The experiments validate the effectiveness and superiority of UHCCN.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingwei You, Nan Xiang, Qilin Wang, and Zhenguo Wang "Unsupervised hypergraph convolutional clustering networks", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 1293716 (29 November 2023); https://doi.org/10.1117/12.3013388

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