A parallelizable model-based approach for marginal and multivariate clustering

Miguel de Carvalho, Gabriel Martos Venturini, Andrej Svetlošák

This paper develops a clustering method that takes advantage of the sturdiness of model-based clustering, while attempting to mitigate some of its pitfalls. First, we note that sta…