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A deep clustering by multi-level feature fusion

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Abstract

Deep clustering extracts non-linear features through neural networks to improve the clustering performance. At present, deep clustering algorithms mostly only use single-level features for clustering, ignoring shallow features information. To address this issue, we propose a joint learning framework that combines features extraction, features fusion and clustering. Different levels of features are extracted through dual convolutional autoencoders and fused. Moreover, the clustering loss function jointly updates the dual network parameters and cluster centers. The experimental results show that the proposed network architecture fusing different levels of features effectively improves clustering results without increasing model complexity. Compared with traditional and deep clustering algorithms, the Clustering Accuracy (ACC) and the Normalized Mutual Information (NMI) metrics are significantly improved.

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Acknowledgements

This work is supported by the National Natural Science Foundations of China (No.61976216 and No.61672522).

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Haiwei Hou, Shifei Ding & Xiao Xu

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding

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  1. Haiwei Hou
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  2. Shifei Ding
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  3. Xiao Xu
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Correspondence to Shifei Ding or Xiao Xu.

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Hou, H., Ding, S. & Xu, X. A deep clustering by multi-level feature fusion. Int. J. Mach. Learn. & Cyber. 13, 2813–2823 (2022). https://doi.org/10.1007/s13042-022-01557-z

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