Main Content
Lembaga Kita Journal Portal
Open Access Articles

AutoClusterAPI: A Lightweight Backend Framework for Automated Unsupervised Clustering Pipelines

  • Yoppy Yunhasnawa1*
  • Atif Windawati2
  • Toga Aldila Cinderatama3
  • Moch. Zawaruddin Abdullah4
  • Elok Nur Hamdana5
1,3,4,5 State Polytechnic of Malang image/svg+xml
2 Politeknik Negeri Semarang image/svg+xml
* Corresponding Author: Yoppy Yunhasnawa
Cover IJSECS VOLUME 6 NOMOR 1 APRIL 2026

Abstract

This study presents AutoClusterAPI, a lightweight and extensible backend system designed to simplify and accelerate unsupervised clustering workflows. The system addresses a recurring problem in data analysis practice: many practitioners need rapid clustering capabilities but lack the programming or statistical background required to build complete pipelines from scratch. AutoClusterAPI provides an automated, endpoint-driven solution that allows users to perform every stage of clustering — from data loading and cleaning to feature preparation, algorithm execution, profiling, and visualization — through standard HTTP requests. The system is built using Python and the FastAPI web framework, supports eight clustering algorithms, and includes automated preprocessing alongside PCA-based visualization. Functional testing confirms that all endpoints behave correctly under both valid and invalid inputs, establishing the reliability of the system. A case study using a customer segmentation dataset further demonstrates its practical utility, showing that AutoClusterAPI can efficiently generate meaningful cluster structures and interpretable visual outputs. The system offers an accessible yet configurable environment for rapid clustering analysis and establishes a basis for future extensions and real-world deployment.

Keywords

Clustering Pipeline Backend Automation FastAPI Unsupervised Learning PCA Visualization Data Preprocessing API-Driven Analytic

How to Cite

Citation Format
Yunhasnawa, Y., Windawati, A., Aldila Cinderatama, T., Abdullah, M. Z., & Nur Hamdana, E. (2026). AutoClusterAPI: A Lightweight Backend Framework for Automated Unsupervised Clustering Pipelines. International Journal Software Engineering and Computer Science (IJSECS), 6(1), 1-14. https://doi.org/10.35870/ijsecs.v6i1.5997

References

31 References
  • Aggarwal, C. C. (2016). An introduction to recommender systems. In Recommender systems: The textbook (pp. 1-28). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-29659-3_1
  • Agrawal, K. P., Garg, S., Sharma, S., & Patel, P. (2016). Development and validation of OPTICS based spatio-temporal clustering technique. Information Sciences, 369, 388-401. https://doi.org/10.1016/j.ins.2016.06.048.
  • Ahmed, M., Seraj, R., & Islam, S. M. S. (2020). The k-means algorithm: A comprehensive survey and performance evaluation. Electronics, 9(8), 1295. https://doi.org/10.3390/electronics9081295
  • Alla, M. (2025). Designing High-Throughput FastAPI Gateways for Microservice Communication. Journal of Computer Science and Technology Studies, 7(7), 823-828. https://doi.org/10.32996/jcsts.2025.7.7.88.
  • Amershi, S., Cakmak, M., Knox, W. B., & Kulesza, T. (2014). Power to the people: The role of humans in interactive machine learning. AI magazine, 35(4), 105-120.
  • Aravind, H., Rajgopal, C., & Soman, K. (2010). A simple approach to clustering in excel. International Journal of Computer Applications, 11(7), 19–25.
  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58. https://doi.org/10.1145/1541880.1541882.
  • Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., Možina, M., Polajnar, M., Toplak, M., Starič, A., Štajdohar, M., Umek, L., Žagar, L., Žbontar, J., Žitnik, M., & Zupan, B. (2013). Orange: Data mining toolbox in Python. Journal of Machine Learning Research, 14(1), 2349–2353.
  • Deng, D. (2020, September). DBSCAN clustering algorithm based on density. In 2020 7th international forum on electrical engineering and automation (IFEEA) (pp. 949-953). IEEE. https://doi.org/10.1109/IFEEA51475.2020.00199.
  • Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press.
  • Franciska, I., & Swaminathan, B. (2017, May). Churn prediction analysis using various clustering algorithms in KNIME analytics platform. In 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS) (pp. 166-170). IEEE. https://doi.org/10.1109/SSPS.2017.8071585.
  • Garg, S., Ahuja, R., Singh, R., & Perl, I. (2024). An effective deep learning architecture leveraging BIRCH clustering for resource usage prediction of heterogeneous machines in cloud data center. Cluster Computing, 27(5), 5699-5719. https://doi.org/10.1007/s10586-023-04258-6.
  • Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media.
  • Greenacre, M., Groenen, P. J., Hastie, T., d’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1), 100. https://doi.org/10.1038/s43586-022-00184-w.
  • Hajihosseinlou, M., Maghsoudi, A., & Ghezelbash, R. (2024). Intelligent mapping of geochemical anomalies: Adaptation of DBSCAN and mean-shift clustering approaches. Journal of Geochemical Exploration, 258, 107393. https://doi.org/10.1016/j.gexplo.2024.107393.
  • Kablan, M., Caldwell, B., Han, R., Jamjoom, H., & Keller, E. (2015). Stateless network functions. In Proceedings of the 2015 ACM SIGCOMM Workshop on Hot Topics in Middleboxes and Network Function Virtualization (pp. 49–54). ACM. https://doi.org/10.1145/2785989.2785993
  • Khoa, N. L. D., & Chawla, S. (2011). Large scale spectral clustering using approximate commute time embedding. arXiv preprint arXiv:1111.4541.
  • Li, Y., Huang, J., & Liu, W. (2016, February). Scalable sequential spectral clustering. In Proceedings of the AAAI conference on artificial intelligence (Vol. 30, No. 1). https://doi.org/10.1609/aaai.v30i1.10298.
  • Marchev, V. (2021). An empirical exploration of data segmentation and clustering methods: Insights from SPSS Modeler. Vanguard Scientific Instruments in Management, 17(1), 1314–0582.
  • Martin-Lopez, A., Segura, S., & Ruiz-Cortés, A. (2021, July). RESTest: automated black-box testing of RESTful web APIs. In Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis (pp. 682-685). https://doi.org/10.1145/3460319.3469082.
  • Peralta, J. H. (2023). Microservice APIs: Using Python, Flask, FastAPI, OpenAPI and more. Simon and Schuster.
  • Pourkamali-Anaraki, F. (2020). Scalable spectral clustering with Nyström approximation: Practical and theoretical aspects. IEEE Open Journal of Signal Processing, 1, 242-256. https://doi.org/10.1109/OJSP.2020.3039330.
  • Raya-Tapia, A. Y., López-Flores, F. J., Ramírez-Márquez, C., & Ponce-Ortega, J. M. (2025). Programming for Clustering: Python, R, and MATLAB. In Machine Learning and Clustering for a Sustainable Future: Applications in Engineering and Environmental Science (pp. 51-99). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-03876-0_3.
  • Tang, C., Li, Z., Wang, J., Liu, X., Zhang, W., & Zhu, E. (2022). Unified one-step multi-view spectral clustering. IEEE Transactions on Knowledge and Data Engineering, 35(6), 6449-6460. https://doi.org/10.1109/TKDE.2022.3172687.
  • Tokuda, E. K., Comin, C. H., & Costa, L. D. F. (2022). Revisiting agglomerative clustering. Physica A: Statistical mechanics and its applications, 585, 126433. https://doi.org/10.1016/j.physa.2021.126433.
  • Tragura, S. J. C. (2022). Building Python microservices with FastAPI: Build secure, scalable, and structured Python microservices from design concepts to infrastructure. Packt Publishing.
  • vetrirah. (2025). Customer — Customer segmentation dataset [Dataset]. Kaggle. https://www.kaggle.com/datasets/vetrirah/customer
  • Wu, L., Chen, P. Y., Yen, I. E. H., Xu, F., Xia, Y., & Aggarwal, C. (2018, July). Scalable spectral clustering using random binning features. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2506-2515). https://doi.org/10.1145/3219819.3220090.
  • Xu, D., & Tian, Y. (2015). A comprehensive survey of clustering algorithms. Annals of data science, 2(2), 165-193. https://doi.org/10.1007/s40745-015-0040-1.
  • Yang, M. S., Lai, C. Y., & Lin, C. Y. (2012). A robust EM clustering algorithm for Gaussian mixture models. Pattern recognition, 45(11), 3950-3961. https://doi.org/10.1016/j.patcog.2012.04.031.
  • Yuan, B. (2025, May). Enhanced Supply Chain Risk Management Using K-Means Clustering and Tableau: A Hybrid Framework for Big Data Visualization. In 2025 2nd International Conference on Intelligent Computing and Robotics (ICICR) (pp. 509-513). IEEE. https://doi.org/10.1109/ICICR65456.2025.00094.

Author Biographies

Yoppy Yunhasnawa, State Polytechnic of Malang

Politeknik Negeri Malang, Malang City, East Java Province, Indonesia

Atif Windawati, Politeknik Negeri Semarang

Politeknik Negeri Semarang, Semarang City, Central Java Province, Indonesia

Toga Aldila Cinderatama, State Polytechnic of Malang

Politeknik Negeri Malang, Malang City, East Java Province, Indonesia

Moch. Zawaruddin Abdullah, State Polytechnic of Malang

Politeknik Negeri Malang, Malang City, East Java Province, Indonesia

Elok Nur Hamdana, State Polytechnic of Malang

Politeknik Negeri Malang, Malang City, East Java Province, Indonesia

License

Copyright (c) 2026 Yoppy Yunhasnawa, Atif Windawati, Toga Aldila Cinderatama, Moch. Zawaruddin Abdullah, Elok Nur Hamdana

Creative Commons License

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

This is an open-access journal distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, reproduction, and adaptation in any medium (even commercially), provided appropriate credit is given to the original author(s), a link to the license is provided, and any changes made are indicated. Authors retain copyright without restrictions and grant the journal the right of first publication as well as non-exclusive publishing rights for archiving, indexing, and scholarly dissemination.

Downloads

OJS UsageStats Plugin
Download data is not yet available.

Similar Articles

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

Contrimetric

Recommended Articles

Journal Transparency

Additional Information

Learn more about how this journal ensures quality, ethics, and transparency.