Salient Bayesian Clustering for Proportional Data via a Multivariate Beta Mixture Model
Abstract
Neighborhoods are multifaceted entities whose attributes span sociocultural, economic, and environmental dimensions. Clustering neighborhoods based on social determinants of health (SDoH) can inform the allocation of public health resources and interventions tailored to community needs. Continuous and bounded data, as seen in aggregated neighborhood-level SDoH data, can pose challenges for widely used clustering methods (e.g., k-means or Gaussian mixture models) that rely on Euclidean distances or normality assumptions. Moreover, not all SDoH variables contribute equally to meaningfully characterize neighborhood clusters, and the inclusion of irrelevant or weakly informative determinants can obscure the underlying structure. To address this issue, we propose a Bayesian Salient Multivariate Beta mixture (SAMBA-Mix) model that flexibly models proportional data with embedded feature saliency to identify relevant variables for clustering. Our model showed improved accuracy in cluster identification and structure, compared to existing models. Using data from the U.S. Agency for Healthcare Research and Quality(AHRQ) SDoH database, we demonstrate the utility of our model to derive neighborhood-level SDoH profiles in Massachusetts and identify key determinants that best characterize differences amongst the different profiles.