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Attention-based hierarchical denoised deep clustering network

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Abstract

Clustering is a basic task of data analysis and decision making. Recently, graph convolution network (GCN) based deep clustering frameworks have produced the state-of-the-art performance. However, the traditional GCN has not fully learnt the structural information of the neighbors. Therefore, in this paper, we propose an attention-based hierarchical denoised deep clustering (AHDDC) algorithm to solve the problem, which enables GCN to learn multiple layers of hidden information and uses the attention mechanism to strengthen the information. Besides, we use a denoising autoencoder to reduce the influence of the data noise on the clustering. In AHDDC, Firstly, we input the feature vector of the original data into a denoising autoencoder (DAE) to learn the hidden representation; secondly, the representation information of the autoencoder and the structure information constructed by the KNN graph are passed into a hierarchical attentional graph convolutional network; finally, a self-supervision module is used to optimize the clustering results. Experimental results show the superiorities of our method over most advanced algorithms. Besides, the effectiveness of the proposed hierarchical, attention based and denoising improving strategies are also verified experimentally.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61902106, in part by the Natural Science Foundation of Tianjin under Grant 19JCZDJC40000, in part by the Natural Science Foundation of Hebei Province under Grant F2020202028 and in part Beihang Beidou technology achievement transformation and industrialization funding project under Grant BARI2001.

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Authors and Affiliations

  1. School of Artifcial Intelligence, Hebei University of Technology, Tianjin, 300401, China

    Yongfeng Dong, Ziqiu Wang, Jiapeng Du & Linhao Li

  2. Hebei Province Key Laboratory of Big Data Computing, Tianjin, 300401, China

    Yongfeng Dong, Ziqiu Wang & Linhao Li

  3. Hebei Engineering Research Center of Data-Driven Industrial Intelligent, Tianjin, 300401, China

    Yongfeng Dong, Ziqiu Wang & Linhao Li

  4. Science and Technology on Micro-system Laboratory, Shanghai Institute of Micro-system and Information Technology, Chinese Academy of Sciences, Shanghai, 201800, China

    Weidong Fang

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This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

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Dong, Y., Wang, Z., Du, J. et al. Attention-based hierarchical denoised deep clustering network. World Wide Web 26, 441–459 (2023). https://doi.org/10.1007/s11280-022-01007-4

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