Online Algorithm for Clustering with Capacity Constraints

Traditional clustering often results in imbalanced clusters, limiting its suitability for real-world problems. In response, capacitated clustering methods have emerged, aiming to achieve balanced clusters by limiting points in each cluster. In this paper, we introduce on-line algorithms with provable bounds on opened centers and cost approximation. We validate our methods experimentally.