Clustering Algorithms in Industry: A Comprehensive Review of Applications, Methods, and Emerging Trends
Eleanor Hawking* , Aleksandra Nowak
Institute for Environmental Science, University of Bristol, UK · Faculty of Physics, Center for Quantum Optical Technologies, Institute of Experimental Physics, University of Warsaw, Poland
* Corresponding author: e.hawking@bristol.ac.uk
Abstract
Clustering algorithms have become indispensable tools in modern industrial data analytics, enabling organizations to extract actionable knowledge from large, heterogeneous datasets without prior class labels. This review paper systematically examines the application of clustering techniques across a diverse spectrum of industry domains, including manufacturing, energy, healthcare finance, and smart infrastructure. Five principal families of clustering algorithms are analyzed: partitional (K-means and its variants), hierarchical, density-based (DBSCAN), model-based (fuzzy C-means and Gaussian mixture models), and graph or spectral approaches. For each family, the theoretical foundations, algorithmic strengths, and documented industrial deployments are discussed. Key application areas surveyed include predictive maintenance, fault detection and diagnosis, customer segmentation, smart grid load profiling, IoT-enabled anomaly detection, and energy consumption optimization. The review further identifies evaluation metrics, open challenges such as scalability and parameter sensitivity, and promising directions, including deep clustering and hybrid meta-heuristic approaches. The findings indicate that no single algorithm universally dominates; instead, successful industrial implementations depend on careful algorithm selection guided by data characteristics, domain knowledge, and computational constraints.
Keywords: Clustering algorithms, Industry 4.0, Predictive maintenance, Fault detection, Smart grid, Unsupervised learning
To cite this article: Hawking, E., & Nowak, A. (2025). Clustering Algorithms in Industry: A Comprehensive Review of Applications, Methods, and Emerging Trends. Journal of Smart Computing , 1(1) , 48-62. https://doi.org/10.5281/zenodo.23424986
References
- Al-Jarrah, O. Y., Al-Hammadi, Y., Yoo, P. D., & Muhaidat, S. (2017). Multi-layered clustering for power consumption profiling in smart grids. IEEE Access, 5, 11269–11278. https://doi.org/10.1109/ACCESS.2017.2712258
- Benabdellah, A. C., Benghabrit, A., & Bouhaddou, I. (2019). A survey of clustering algorithms for an industrial context. Procedia Computer Science, 148, 291–302. https://doi.org/10.1016/j.procs.2019.01.022
- Ding, S., Du, M., & Jia, W. (2021). A weighted fuzzy C-means clustering method with density peak for anomaly detection in IoT-enabled manufacturing process. Journal of Intelligent Manufacturing, 33(5), 1309–1323. https://doi.org/10.1007/s10845-020-01690-y
- Elshenawy, L. M., Chakour, C., & Mahmoud, T. A. (2022). Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes. Journal of the Franklin Institute, 359(13), 7115–7139. https://doi.org/10.1016/j.jfranklin.2022.06.022
- Ezugwu, A. E., Ikotun, A. M., Oyelade, O. O., Abualigah, L., Agushaka, J. O., Eke, C. I., & Akinyelu, A. A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110, Article 104743. https://doi.org/10.1016/j.engappai.2022.104743
- Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., Foufou, S., & Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267–279. https://doi.org/10.1109/TETC.2014.2330519
- Ikotun, A. M., Ezugwu, A. E., Abualigah, L., Abuhaija, B., & Heming, J. (2023). K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622, 178–210. https://doi.org/10.1016/j.ins.2022.11.139
- Jiang, Z., Lin, R., & Yang, F. (2021). An incremental clustering algorithm with pattern drift detection for IoT-enabled smart grid systems. Sensors, 21(19), Article 6466. https://doi.org/10.3390/s21196466
- Liu, S., Dong, L., Liao, X., Cao, X., & Wang, X. (2019). Photovoltaic array fault diagnosis based on a Gaussian kernel fuzzy C-means clustering algorithm. Sensors, 19(7), Article 1520. https://doi.org/10.3390/s19071520
- Molinié, D., Madani, K., & Amarger, V. (2022). Clustering at the disposal of Industry 4.0: Automatic extraction of plant behaviors. Sensors, 22(8), Article 2939. https://doi.org/10.3390/s22082939
- Sinaga, K. P., & Yang, M.-S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796
- Turanoglu Bekar, E., Nyqvist, P., & Skoogh, A. (2020). An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Advances in Mechanical Engineering, 12(5), 1–17. https://doi.org/10.1177/1687814020919207
- Wani, A. A. (2024). Comprehensive analysis of clustering algorithms: Exploring limitations and innovative solutions. PeerJ Computer Science, 10, Article e2286. https://doi.org/10.7717/peerj-cs.2286
- Xu, J., Wang, Y., & Tian, H. (2022). Data clustering: Application and trends. Artificial Intelligence Review, 56(8), 8198–8249. https://doi.org/10.1007/s10462-022-10325-y
- Zhang, M., Jiang, X., Fang, Z., Zeng, Y., & Xu, K. (2019). High-order hidden Markov model for trend prediction in financial time series. Physica A: Statistical Mechanics and Its Applications, 517, 1–12. https://doi.org/10.1016/j.physa.2018.10.053