Loss-based clustering methods, such as k-means clustering and its variants, are standard
tools for finding groups in data. However, the lack of quantification of uncertainty in the
estimated clusters is a disadvantage. Model-based clustering based on mixture models
provides an alternative approach, but such methods face computational problems and are
highly sensitive to the choice of kernel. In this article we propose a generalized Bayes
framework that bridges between these paradigms through the use of Gibbs posteriors. In …