  This paper introduces the multivariate beta mixture model (MBMM), a new
probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes
because of the flexible probability density function of the multivariate beta
distribution. We introduce the properties of MBMM, describe the parameter
learning procedure, and present the experimental results, showing that MBMM
fits diverse cluster shapes on synthetic and real datasets. The code is
released anonymously at https://github.com/hhchen1105/mbmm/.
