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arXiv · 2508.12758

Constrained Centroid Clustering: A Novel Approach for Compact and Structured Partitioning

Sowmini Devi Veeramachaneni·Ramamurthy Garimella
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Abstract

This paper presents Constrained Centroid Clustering (CCC), a method that extends classical centroid-based clustering by enforcing a constraint on the maximum distance between the cluster center and the farthest point in the cluster. Using a Lagrangian formulation, we derive a closed-form solution that maintains interpretability while controlling cluster spread. To evaluate CCC, we conduct experiments on synthetic circular data with radial symmetry and uniform angular distribution. Using ring-wise, sector-wise, and joint entropy as evaluation metrics, we show that CCC achieves more compact clusters by reducing radial spread while preserving angular structure, outperforming standard methods such as K-means and GMM. The proposed approach is suitable for applications requiring structured clustering with spread control, including sensor networks, collaborative robotics, and interpretable pattern analysis.

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Sowmini Devi Veeramachaneni, Ramamurthy Garimella. 2025-08-18. Constrained Centroid Clustering: A Novel Approach for Compact and Structured Partitioning. https://arxiv.org/abs/2508.12758

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