A Review of Clustering Algorithms: Evolution, Comparison, and Challenges from Traditional Clustering to Deep Clustering

Main Article Content

Zihao Fan
School of Computer Science and Engineering, Central South University, Changsha 410083, China

DOI: https://doi.org/10.70267/cai.v3n5.5362

Keywords

clustering algorithm, deep clustering, unsupervised learning

Abstract

Against the background of big data, clustering algorithms, as a core task of unsupervised learning, have been widely applied, and the related theories and technologies have continued to iterate and develop. Existing reviews mostly focus on the classification and comparison of traditional clustering algorithms, while insufficient attention has been paid to the evolutionary relationship between traditional clustering and deep clustering. Therefore, this paper reviews clustering algorithms and their evolutionary logic, systematically sorts out the evolutionary route of clustering algorithms from traditional methods to deep models, describes the relatively classic and main clustering algorithms, and compares and analyzes their advantages and disadvantages. It also shows the key transition from classic clustering to deep clustering. In addition, this paper describes the core challenges that clustering algorithms may face in the future and analyzes further research directions according to current research progress.

Abstract 15 | PDF Downloads 8

References

  • [1]Singh, J., & Singh, D. (2024). A comprehensive review of clustering techniques in artificial intelligence for knowledge discovery: Taxonomy, challenges, applications and future prospects. Advanced Engineering Informatics, 62, Article 102799. https://doi.org/10.1016/j.aei.2024.102799
  • [2]Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., ... & Lin, C. T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681. https://doi.org/10.1016/j.neucom.2017.06.053
  • [3]Wang, Q., Wang, C., Feng, Z. Y., & Ye, J. F. (2012). Review of the k-means clustering algorithm. Electronic Design Engineering, 20(7), 21-24. https://doi.org/10.14022/j.cnki.dzsjgc.2012.07.034
  • [4]Yang, J. C., & Zhao, C. (2019). Review of the k-means clustering algorithm. Computer Engineering and Applications, 55(23), 7-14, 63. https://doi.org/10.3778/j.issn.1002-8331.1908-0347
  • [5]Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8), 651-666. https://doi.org/10.1016/j.patrec.2009.09.011
  • [6]Celebi, M. E., et al. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200-210. https://doi.org/10.1016/j.eswa.2012.07.021
  • [7]Wu, S. H., Cheng, Y., Zheng, Y. N., & Pan, Y. T. (2011). Review of the k-means algorithm. New Technology of Library and Information Service, (5), 28-35.
  • [8]Bhuyan, R., & Borah, S. (2023). A survey of some density-based clustering techniques. arXiv. https://doi.org/10.48550/arXiv.2306.09256
  • [9]Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
  • [10]Deng, L. P. (2019). Review of classic clustering algorithms. Public Communication of Science & Technology, 11(5), 108-110. https://doi.org/10.16607/j.cnki.1674-6708.2019.05.053
  • [11]Watteau, T., Bonnefoy, A., Illouz-Laurent, S., Jusseau, J., & Iovleff, S. (2024). Advanced graph clustering methods: A comprehensive and in-depth analysis. (2024). arXiv. https://doi.org/10.48550/arXiv.2407.09055
  • [12]Sun, J. G., Liu, J., & Zhao, L. Y. (2008). Clustering algorithms research. Journal of Software, 19(1), 48-61. https://doi.org/10.3724/SP.J.1001.2008.00048
  • [13]Wang, C., Li, Y., & Xie, N. (2026). Beyond theory: A comprehensive survey of clustering algorithm applications in wireless and media communications. Digital Communications and Networks. https://doi.org/10.1016/j.dcan.2026.06.004
  • [14]Dong, Y. F., Deng, Y. H., Dong, Y., & Wang, Y. C. (2022). A review of clustering based on deep learning. Journal of Computer Applications, 42(4), 1021-1028. https://doi.org/10.11772/j.issn.1001-9081.2021071275
  • [15]Ji, Q., Sun, Y. F., Hu, Y. L., & Yin, B. C. (2021). Review of deep clustering algorithms. Journal of Beijing University of Technology, 47(8), 912-924. https://doi.org/10.11936/bjutxb2021010013
  • [16]Zhou, S., Xu, H., Zheng, Z., Chen, J., Li, Z., Bu, J., Wu, J., Wang, X., Zhu, W., & Ester, M. (2025). A comprehensive survey on deep clustering: Taxonomy, challenges, and future directions. ACM Computing Surveys, 57(3), Article 69, 1-38. https://doi.org/10.1145/3689036
  • [17]Tao, W. B., Qian, Y. R., Zhang, Y. Y., Ma, H. Z., Leng, H. Y., & Ma, M. N. (2022). Review of deep clustering algorithms based on autoencoders. Computer Engineering and Applications, 58(18), 16-25.
  • [18]Xie, J., Girshick, R., & Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. In Proceedings of the International Conference on Machine Learning (pp. 478-487).
  • [19]Yang, X., Deng, C., Zheng, F., et al. (2019). Deep spectral clustering using dual autoencoder network. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4066-4075). Long Beach, CA, United States.
  • [20]Guo, M. (2024). Research on deep clustering methods based on convolutional residual autoencoders [Master's thesis, Lanzhou University of Finance and Economics].
  • [21]Zhou, K., Huang, X., Song, Q., Chen, R., & Hu, X. (2022). Auto-GNN: Neural architecture search of graph neural networks. Frontiers in Big Data, 5, 1029307.https://doi.org/10.48550/arXiv.1909.03184
  • [22]Chowdhury, M. H., Hirose, Y., Marsland, S., & Yao, Y. (2025). Mixtures of posterior and prior variational autoencoders for representation learning and cluster analysis in latent space. Neurocomputing, 657, Article 131524.
  • [23]Yan, J., Liu, J., & Zhang, Z.-Y. (2025). ClusterDDPM: An EM clustering framework with denoising diffusion probabilistic models. Information Sciences, 720, Article 122518.
  • [24]Ruan, T., Yu, N., Zhao, S., Chen, F., & Huang, Y. (2026). DGVAC: Disentangled Gaussian-vMF alignment for deep clustering. Neural Networks, 203, Article 109114.
  • [25]An, J. X., Liu, Y., & Yang, L. W. (2025). Review of deep clustering based on contrastive learning. Microelectronics & Computer, 42(7), 1-10.
  • [26]Dosovitskiy, A., Springenberg, J. T., Riedmiller, M., & Brox, T. (2014). Discriminative unsupervised feature learning with convolutional neural networks. Advances in neural information processing systems, 27.
  • [27]Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020, November). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597-1607). PmLR.
  • [28]Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020). Unsupervised learning of visual features by contrasting cluster assignments. Advances in neural information processing systems, 33, 9912-9924.
  • [29]Grill, J. B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., ... & Valko, M. (2020). Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33, 21271-21284.
  • [30]Zhang, D., Nan, F., Wei, X., Li, S. W., Zhu, H., McKeown, K., ... & Xiang, B. (2021, June). Supporting clustering with contrastive learning. In Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: Human language technologies (pp. 5419-5430).
  • [31]Li, Y., Hu, P., Liu, Z., Peng, D., Zhou, J. T., & Peng, X. (2021). Contrastive clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 8547–8555). AAAI Press.
  • [32]Xing, R. W. (2024). Research on deep clustering algorithms based on contrastive learning [Master's thesis, Shanxi University of Finance and Economics].
  • [33]Li, H. J. (2024). Research on deep clustering algorithms based on contrastive learning [Master's thesis, Guilin University of Technology].
  • [34]Ju, H., Guo, J., Ding, W., Pedrycz, W., Cheng, X., & Yang, X. (2025). FDGC: Fuzzy deep clustering with dual-granularity contrastive learning. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2025.114401
  • [35]Berahmand, K., Li, Y. F., & Xu, Y. (2023). DAC-HPP: Deep attributed clustering with high-order proximity preserve. Neural Computing and Applications, 35, 24493-24511.
  • [36]Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., & Monfardini, G. (2008). The graph neural network model. IEEE transactions on neural networks, 20(1), 61-80.
  • [37]Huang, X. Y., Zhang, C. H., Zhao, W., & Wang, J. K. (2024). Review of deep clustering research. Journal of Sichuan University of Science & Engineering (Natural Science Edition), 37(4), 47-57.
  • [38]LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
  • [39]Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv. https://arxiv.org/abs/1609.02907
  • [40]Fan, S., Lu, E., Wang, X., Lin, K., Shi, C., & Wang, B. (2020). One2multi graph autoencoder for multi-view graph clustering. In Proceedings of the Web Conference 2020 (pp. 3070-3076). Association for Computing Machinery.
  • [41]Huang, J., Gong, S., & Zhu, X. (2020). Deep semantic clustering by partition confidence maximisation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8849-8858).
  • [42]Chowdhury, A. R., Gupta, A., & Das, S. (2025). Deep multi-view clustering: A comprehensive survey of the contemporary techniques. Information Fusion, 119, Article 103012. https://doi.org/10.1016/j.inffus.2025.103012
  • [43]Jin, X., Chen, M., Wang, Y., & Li, S. (2026). CDMVC: Consensus-driven multiview clustering via adaptive hierarchical matrix factorization with dynamic view fusion. IEEE Internet of Things Journal, 13(8), 16480-16495. https://doi.org/10.1109/JIOT.2026.3659704
  • [44]Wang, S., Liu, C., & Xing, S. J. (2022). Review of the k-means clustering algorithm. Journal of East China Jiaotong University, 39(5), 119-126.
  • [45]Wani, A. A. (2024). Comprehensive analysis of clustering algorithms: exploring limitations and innovative solutions. PeerJ Computer Science, 10, e2286.
  • [46]Li, Y., Cai, J., Yang, H., Yang, Y., Wang, J., & Hao, J. (2026). A survey of contrastive clustering research: algorithms, applications and challenges. Complex & Intelligent Systems. https://doi.org/10.1007/s40747-026-02413-1
  • [47]Lu, Y., Li, H., Li, Y., Lin, Y., & Peng, X. (2024). A survey on deep clustering: from the prior perspective. Vicinagearth, 1(1), 4. https://doi.org/10.1007/s44336-024-00001-w
  • [48]Li, J. T. (2019). Research on density clustering algorithms and their application in ocean mesoscale phenomena [Master's thesis, Shandong University of Science and Technology].
  • [49]Huang, Y. Y., Tursun, W., Tu, R. X., Zhang, X., & Chen, X. K. (2025). Research on traditional block building classification and style renovation strategies based on clustering algorithms: A case study of Laozheng Street in Dongshi Town. Huazhong Architecture, 43(4), 120–123.
  • [50]Han, Q. H., Ma, Q., Liu, M., & Xu, J. (2021). Damage diagnosis of spatial grid structures based on natural-frequency clustering analysis under temperature changes. Journal of East China Jiaotong University, 38(4), 8-17.
  • [51]Nawaz, M., Mehmood, Z., Nazir, T., Naqvi, R. A., Rehman, A., Iqbal, M., & Saba, T. (2022). Skin cancer detection from dermoscopic images using deep learning and fuzzy k‐means clustering. Microscopy research and technique, 85(1), 339-351.
  • [52]Wang, R., Wang, P., Li, L., & Liu, P. (2023, May). Community detection with graph convolutional auto-encoder and deep clustering. In 2023 26th international conference on computer supported cooperative work in design (CSCWD) (pp. 1589-1594). IEEE.

Article Sidebar

Full Text
PDF
Information
Copy Publication Information
Published
Oct 29, 2026
Issue
Vol. 3 No. 5 (2026)
Section
Research Articles
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Fan, Z. (2026). A Review of Clustering Algorithms: Evolution, Comparison, and Challenges from Traditional Clustering to Deep Clustering. Computers and Artificial Intelligence, 3(5), 53-62. https://doi.org/10.70267/cai.v3n5.5362