Product Sales Optimization with DBSCAN Clustering Algorithm on e-commerce Dataset
DOI:
https://doi.org/10.62205/mjgcs.v3i2.62Keywords:
E-commerce, Sales Optimization, Clustering, DBSCAN, Customer Segmentation, Marketing StrategiesAbstract
This research aims to optimize product sales strategies in e-commerce businesses by utilizing the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm. DBSCAN is applied to a customer transaction dataset to group customers into segments based on their purchasing patterns, represented by the Quantity and UnitPrice variables.
The clustering results demonstrate that DBSCAN effectively identifies customer segments with distinct characteristics, such as loyalty, price sensitivity, and product preferences. This information is then used to design targeted sales strategies, including personalized product recommendations, relevant promotional offers, and efficient inventory management.
Implementing DBSCAN-based optimization strategies is expected to improve sales, profitability, and customer satisfaction. This research highlights the effectiveness of the DBSCAN clustering algorithm as a valuable tool for understanding customer behavior and optimizing sales strategies in e-commerce businesses.
References
Ester, M., Kriegel, H. P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96) (pp. 226-231).
Han, J., Pei, J., & Kamber, M. (2011). Data mining: Concepts and techniques. Morgan Kaufmann.
Tan, P. N., Steinbach, M., & Kumar, V. (2005). Introduction to data mining. Pearson Education.
Kotlarczyk, D. (2017). DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Towards Data Science.
Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems, 42(3), 1–21. https://doi.org/10.1145/3068335
Ankerst, M., Breunig, M. M., Kriegel, H. P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. In Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data (pp. 49–60). https://doi.org/10.1145/304182.304187
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
Davies, D. L., & Bouldin, D. W. (1979). A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1(2), 224–227. https://doi.org/10.1109/TPAMI.1979.4766909
Irawan, F., et al. (2025). E-commerce customer segmentation based on RFM analysis using DBSCAN algorithm to improve marketing strategy. Jurnal EMT KITA, 9(4), 1850–1859. https://doi.org/10.35870/emt.v9i4.5375
Snehalatha, N., et al. (2023). Customer segmentation and profiling for e-commerce using DBSCAN and Fuzzy C-Means. Proceedings on Engineering Sciences, 5(3), 539–544. https://doi.org/10.24874/PES05.03.016
Paramita, A. S. (2024). Comparison of K-Means and DBSCAN algorithms for customer segmentation in e-commerce. Journal of Digital Market and Digital Currency, 1(1), 43–62. https://doi.org/10.47738/jdmdc.v1i1.3
Saputra, J. N., et al. (2025). A comparative analysis of K-Means++ and DBSCAN clustering algorithms for customer segmentation based on behaviour in Indonesian e-commerce. In 2025 IEEE International Conference on Advanced Information Scientific Development (ICAISD) (pp. 175–180). IEEE. https://doi.org/10.1109/ICAISD68166.2025.11385769
Şahinbaş, K. (2022). Performance comparison of K-Means and DBSCAN methods for airline customer segmentation. Black Sea Journal of Engineering and Science, 5(4), 158–165. https://doi.org/10.34248/bsengineering.1170943
Sadikin, M., et al. (2025). Analysis of order data customer segmentation in logistics companies using K-Medoids and DBSCAN algorithms. Technologia Journal, 2(4), 21–29. https://doi.org/10.62872/d6hbsb69
Nair, A. S. (2025). Customer segmentation using K-Means clustering in unsupervised machine learning. International Journal of Science and Research (IJSR), 14(4), 1376–1379. https://doi.org/10.21275/SR25417125301
Lavanya, M., et al. (2025). Improving decision-making in retail: Employing DBSCAN algorithm for enhanced customer segmentation in mall environments. In 2025 International Conference on Frontier Technologies and Solutions (ICFTS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICFTS62006.2025.11031670
Chen, Z. (2024). The investigation of machine learning approaches for customer segmentation. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (pp. 71–76). https://doi.org/10.5220/0013206800004568
Zhang, Y. (2024). Machine learning-based customer segmentation: A comprehensive investigation of techniques, challenges and applications. In Proceedings of the 1st International Conference on E-commerce and Artificial Intelligence (pp. 97–101). https://doi.org/10.5220/0013207200004568
Pratheepraj, S., et al. (2025). Temporal clustering for e-commerce trends: A machine learning approach to customer behaviour analysis. International Journal of Research Publication and Reviews, 6(9), 4315–4322. https://doi.org/10.55248/gengpi.6.0925.3501
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Daisy Larisa, Firdaus, ahmad rafael
This work is licensed under a Creative Commons Attribution 4.0 International License.
