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| Research Progress of Deep Clustering Based on Unsupervised Representation Learning |
| HOU Haiwei1, DING Shifei1,2, XU Xiao1,2 |
1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116;
2. Engineering Research Center of Mine Digitization of Ministry of Education, China University of Mining and Technology, Xuzhou 221116 |
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Abstract In the era of big data, data usually has the characteristics of large scale, high dimension and complex structure. Deep learning is utilized to combine representation learning and clustering tasks in deep clustering. Therefore, the performance of deep clustering for large-scale and high-dimensional data is greatly improved. The development of deep clustering is rarely summarized from the perspective of representation learning. The difference between traditional and deep clustering algorithms and the heterogeneity of deep clustering algorithms are seldom analyzed. Firstly, common clustering algorithms in deep clustering are summarized. Deep clustering algorithms are divided into generative and discriminative models based deep clustering algorithms, and representation learning process of deep models in clustering tasks is analyzed. Secondly, the comparative analysis of multiple types of algorithms is carried out through experiments. And the advantages and disadvantages of different algorithms are summarized to select models for specific tasks. Finally, application scenarios are described and the future development trend of deep clustering is discussed.
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Received: 30 August 2022
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| Fund:National Natural Science Foundation of China(No.61976216,62276265,61672522) |
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Corresponding Authors:
DING Shifei, Ph.D., professor. His research interests include pattern recognition, machine learning and data mining.
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| About author:: HOU Haiwei, Ph.D. candidate. Her research interests include machine learning, deep learning and deep clustering. XU Xiao, Ph.D., lecturer. Her research interests include machine learning and clustering analysis. |
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2022, Vol. 35 
