Bayesian Distance Clustering

Understand

Model-based clustering is widely-used in a variety of application areas.

• However, fundamental concerns remain about robustness.
• In particular, results can be sensitive to the choice of kernel representing the within-cluster data density.
• Leveraging on properties of pairwise differences between data points, we propose a class of Bayesian distance clustering methods, which rely on modeling the likelihood of the pairwise distances in place of the original data.