Asymmetric clustering using the alpha–beta 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.

We propose the use of an asymmetric dissimilarity measure in centroid-based clustering. The dissimilarity employed is the Alpha–Beta divergence (AB-divergence), which can be asymmetrized using its parameters. We compute the degree of asymmetry of the AB-divergence on the basis of the within-cluster variances. In this way, the proposed approach is able to flexibly model even clusters with significantly different variances. Consequently, this method overcomes one of the major drawbacks of the standard symmetric centroid-based clustering.