Filipe F. R. Damasceno, Marcelo B. A. Veras, D. Mesquita, J. Gomes, Carlos Brito
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Shrinkage k-Means: A Clustering Algorithm Based on the James-Stein Estimator
In this work, we propose Shrinkage k-means (Sk-means), a novel variant of k-means based on the James-Stein estimator for the mean of a multivariate normal given a single sample point. We evaluate Sk-means on both synthetic and real-world data. The proposed method outperformed standard clustering methods and also the existing method based on k-means which uses the James-Stein estimator. Results also suggest that Sk-means is robust to outliers.