Constrained Centroid Clustering: A Novel Approach for Compact and Structured Partitioning
This is an incremental method for applications requiring structured clustering with spread control, such as sensor networks and collaborative robotics.
The paper tackles the problem of achieving compact and structured clustering by introducing Constrained Centroid Clustering (CCC), which enforces a maximum distance constraint to control cluster spread, and shows that it outperforms standard methods like K-means and GMM in reducing radial spread while preserving angular structure on synthetic data.
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.