Published Paper Details:

Customer segmentation is a cornerstone of data-driven marketing, personalization, and decision-making. With the rapid growth of digital platforms, organizations now handle large-scale, high-dimensional customer data, making traditional segmentation techniques insufficient. This analytical study examines major clustering algorithms used for customer segmentation, evaluates their suitability for large datasets, and compares their performance in terms of scalability, accuracy, interpretability, and computational complexity. The proposed framework leverages Mini-Batch K-Means, a batch-based learning algorithm that processes data incrementally in small subsets, significantly reducing memory usage and improving execution speed. Experimental results demonstrate that the proposed scalable clustering framework achieves faster convergence and better resource utilization compared to conventional techniques, proving suitable for real-world big data applications.