Asymmetric clustering using the alphabeta divergence
We propose the use of asymmetric dissimilarity in centroid-based clustering. We asymmetrize the AB-divergence on the basis of the within-cluster variances. The proposed approach efficiently takes into consideration clusters with different variances. Experiments were conducted on real and simulated data of high and low dimensionality.
clustering alphabeta divergence dissimilarity asymmetry