Application of Deep Learning Algorithms in Clustering Production Units: Enhancing Regulatory Processes Using Soft Computing Approaches (Case Study: North Khorasan Province)

Document Type : Original Article

Authors

1 Assistant Professor, Department of Computer Engineering, Kosar University of Bojnord, Bojnord, Iran

2 Assistant Professor, Department of Industrial Engineering, Kosar University of Bojnord, Bojnord, Iran

10.22091/jemsc.2026.13325.1286

Abstract

The oversight of production units ensures compliance with national and international standards. The National Standards Organization gathers the quality documentation of manufacturers within the SINA system. Despite this extensive database, the intensity and frequency of sampling and inspection processes are conducted without considering quality records. This study develops an intelligent clustering and deep learning approach to categorize production units in North Khorasan. In this regard, algorithms such as Spectral clustering, Density-based hierarchical clustering with noise detection, Deep embedded clustering, Gaussian mixture model, and Louvain were implemented. The Davies–Bouldin index, Average Silhouette score, and Calinski–Harabasz index were utilized to assess the quality of clusters. Furthermore, the efficacy of the chosen approach was contrasted with the findings of earlier studies (K-means). The results showed that Deep embedded clustering outperformed, revealing hidden data structures with more cohesive, separable clusters and superior metrics. Deep embedded clustering outperformed K-means, reducing Davies–Bouldin by 0.24, increasing silhouette by 0.052, and enhancing Calinski–Harabasz by 479. It identified five distinct production clusters by location, production volume, and quality metrics, enabling more efficient monitoring.

Keywords

Main Subjects

References

Caliński, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics-Theory and Methods, 3(1), 1–27. https://doi.org/10.1080/03610927408827101
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-based clustering based on hierarchical density estimates. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 160–172). Springer. https://doi.org/10.1007/978-3-642-37456-2_14
Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep clustering for unsupervised learning of visual features. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 132–149).
Chen, K., Zhang, P., Yan, H., Chen, G., Sun, T., Lu, Q., Chen, Y., & Shi, H. (2024). A review of machine learning in additive manufacturing: Design and process. The International Journal of Advanced Manufacturing Technology, 135(3), 1051–1087. https://doi.org/10.1007/s00170-024-13094-w
Combe, D., Largeron, C., Géry, M. & Egyed-Zsigmond, E., )2015, October (. I-louvain: An attributed graph clustering method. In International Symposium On Intelligent Data Analysis (pp. 181-192). Springer International Publishing.
Davies, D. L., & Bouldin, D. W. (2009). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909
Dogan, A., & Birant, D. (2021). Machine learning and data mining in manufacturing. Expert Systems with Applications, 166, 114060. https://doi.org/10.1016/j.eswa.2020.114060
Dollmann, M.M., )2023(. Graph Clustering: A comparison of Louvain and Leiden. In Conf. Ser (Vol. 2129, p. 012028).
Ghousi, R. (2015). Applying a decision support system for accident analysis by using data mining approach: A case study on one of the Iranian manufactures. Journal of Industrial and Systems Engineering, 8(3), 59-76.
Guo, X., Liu, X., Zhu, E., & Yin, J. (2017). Deep clustering with convolutional autoencoders. In Neural Information Processing (pp. 373–382). Springer. https://doi.org/10.1007/978-3-319-70096-0_39
Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2–3), 107–145. https://doi.org/10.1023/A:1012801612483
Harding, J. A., Shahbaz, M., Srinivas, & Kusiak, A. (2006). Data mining in manufacturing: A review. Journal of Manufacturing Science and Engineering, 128(4), 969–976. https://doi.org/10.1115/1.2194554
Jacobs, A. (2009). The pathologies of big data. Communications of the ACM, 52(8), 36–44. https://doi.org/10.1145/1536616.1536632
Kampezidou, S. I., Ray, A. T., Bhat, A. P., Fischer, O. J. P., & Mavris, D. N. (2024). Fundamental components and principles of supervised machine learning workflows with numerical and categorical data. Eng, 5(1), 384–416. https://doi.org/10.3390/eng5010021
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis. John Wiley & Sons.
Khadivar, A., & Mojibian, F. (2022). Workshops clustering using a combination approach of data mining and MCDM. Modern Researches in Decision Making, 7(2), 1–20.
Kusiak, A. (2017). Smart manufacturing must embrace big data. Nature, 544(7648), 23–25. https://doi.org/10.1038/544023a
Liu, J., & Han, J. (2018). Spectral clustering. In Data clustering (pp. 177–200). Chapman and Hall/CRC.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.
McInnes, L., Healy, J., & Astels, S. (2017). HDBSCAN: Hierarchical density-based clustering. Journal of Open Source Software, 2(11), 205. https://doi.org/10.21105/joss.00205
Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50(2), 159–179. https://doi.org/10.1007/BF02294245
Molaee Fard, R. (2023). Provide a method to diagnose and optimize diabetes using data mining methods and firefly algorithm. Engineering Management and Soft Computing, 9(1), 36-48. https://doi.org/10.22091/JEMSC.2022.6575.1147
Ng, A., Jordan, M., & Weiss, Y. (2001). On spectral clustering: Analysis and an algorithm. Advances in Neural Information Processing Systems, 14, 849–856.
Niavand, M., Adibi, M. A., & Pourghader Chobar, A. (2024). Selection of green supplier by multi-moora combination method and two-stage clustering. Engineering Management and Soft Computing, 10(1), 14-49. https://doi.org/10.22091/jemsc.2024.10977.1181
Pakzad, A., Vahdani, M., & Khoran, M. (2024). Personalization of sampling and standard inspection based on data mining. In The 10th International Conference on Industrial Engineering and Systems. Mashhad, Iran. https://civilica.com/doc/2119451
Rahimi, F., Kamranrad, R., & Zarei, A. (2022). Design of integrated clustering-association data mining model to study the electricity consumption behavior of industrial units. Iranian Journal of Energy, 25(3), 65–78.
Raptis, T. P., Passarella, A., & Conti, M. (2019). Data management in Industry 4.0: State of the art and open challenges. IEEE Access, 7, 97052–97093. https://doi.org/10.1109/ACCESS.2019.2929291
Reynolds, D., )2015(. Gaussian mixture models. In Encyclopedia of biometrics (pp. 827-832). Springer. https://doi.org/10.1007/978-0-387-73003-5_196
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Sheikh Shoaee, H. (2021). A review of machine learning and data mining in the manufacturing industry. In The 2nd National Conference on Management and Tourism Industry. Tehran, Iran.
Song, H., Li, C., Fu, Y., Li, R., Zhang, H., & Wang, G. (2023). A two-stage unsupervised approach for surface anomaly detection in wire and arc additive manufacturing. Computers in Industry, 151, 103994. https://doi.org/10.1016/j.compind.2023.103994
Suman, S., & Das, A. (2020). Fuzzy clustering-based process-monitoring strategy for a multistage manufacturing facility. In Soft Computing: Theories and Applications: Proceedings of SoCTA 2018 (pp. 459-469). Springer Singapore.
Tran, T.-H., Cao, T.-D., & Tran, T.-T.-H. (2021). HDBSCAN: Evaluating the performance of hierarchical clustering for big data. In Soft computing: Biomedical and related applications (pp. 273–283). Springer. https://doi.org/10.1007/978-3-030-73103-8_20
Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and Computing, 17(4), 395–416. https://doi.org/10.1007/s11222-007-9033-z
Wang, Y., Chen, Q., Kang, C., & Xia, Q. (2016). Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Transactions on Smart Grid, 7(5), 2437–2447. https://doi.org/10.1109/TSG.2016.2548565
Xie, J., Girshick, R., & Farhadi, A. (2016). Unsupervised deep embedding for clustering analysis. In International Conference on Machine Learning (pp. 478–487). PMLR.
Younespour, M. S., & Romoozi, M. (2023). wireless sensor network clustering based on label propagation algorithm. Engineering Management and Soft Computing, 8(2), 16-29.
Zidek, K., Maxim, V., Pitel, J., & Hosovsky, A. (2016). Embedded vision equipment of industrial robot for inline detection of product errors by clustering–classification algorithms. International Journal of Advanced Robotic Systems, 13(5), 1–14. https://doi.org/10.1177/1729881416664901
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics