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

• URL: http://arxiv.org/abs/2508.12758v1
• Date: Mon, 18 Aug 2025 09:30:54 GMT
• Title: Constrained Centroid Clustering: A Novel Approach for Compact and Structured Partitioning
• Authors: Sowmini Devi Veeramachaneni, Ramamurthy Garimella,
• Abstract summary: Constrained Centroid Clustering (CCC) is a method that extends classical centroid-based clustering.<n>CCC achieves more compact clusters by reducing radial spread while preserving angular structure.<n>The proposed approach is suitable for applications requiring structured clustering with spread control, including sensor networks, collaborative robotics, and interpretable pattern analysis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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