Understanding uncertainty in Bayesian cluster analysis

Abstract

The Bayesian approach to clustering is often appreciated for its ability to provide uncertainty in the partition structure. However, summarizing the posterior distribution over the clustering structure can be challenging, due the discrete, unordered nature and massive dimension of the space. While recent advancements provide a single clustering estimate to represent the posterior, this ignores uncertainty and may even be unrepresentative in instances where the posterior is multimodal. To enhance our understanding of uncertainty, we propose a WASserstein Approximation for Bayesian clusterIng (WASABI), which summarizes the posterior samples with not one, but multiple clustering estimates, each corresponding to a different part of the partition space that receives substantial posterior mass. Specifically, we find such clustering estimates by approximating the posterior distribution in a Wasserstein distance sense, equipped with a suitable metric on the partition space. An interesting byproduct is that a locally optimal solution can be found using a k-medoids-like algorithm on the partition space to divide the posterior samples into groups, each represented by one of the clustering estimates. Using synthetic and real datasets, we show that WASABI helps to improve the understanding of uncertainty, particularly when clusters are not well separated or when the employed model is misspecified.