V. Novoselac

Mahalanobis-Constrained Hard-Clustering Algorithm

The paper proposes a modification of the standard k-means algorithm that removes low-probability tail points of Gaussian components using the p-quantile of the chi-squared distribution as a trimming threshold based on the squared Mahalanobis distance. The procedure is evaluated on synthetic noisy datasets and compared...