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Penalized K-Means Clustering: Another Look at Its Statistical Properties

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Proposed penalized K-Means method improves clustering outcomes, indicating effective model parsimony.

The proposed penalized K-Means method reduces overfitting while enhancing accuracy in identifying clusters.
Simulation studies indicate improved performance metrics, accurately identifying the true number of clusters.
The method addresses K-Means' convergence issues, providing robust solutions for clustering with unknown K.
Application to globular galaxy datasets showcases the method's effectiveness in handling large-scale data.

Banerjee et al. (2025) studied this question.

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View Full Paper

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Authors

Proposed penalized K-Means method improves clustering outcomes, indicating effective model parsimony.

The proposed penalized K-Means method reduces overfitting while enhancing accuracy in identifying clusters.
Simulation studies indicate improved performance metrics, accurately identifying the true number of clusters.
The method addresses K-Means' convergence issues, providing robust solutions for clustering with unknown K.
Application to globular galaxy datasets showcases the method's effectiveness in handling large-scale data.

Banerjee et al. (2025) studied this question.

View Full Paper

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