Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes
This is an incremental improvement for clustering tasks, offering a new method for flexible cluster shapes.
The paper tackles the problem of probabilistic clustering by introducing the multivariate beta mixture model (MBMM), which adapts to diverse cluster shapes, and shows that it fits such shapes on synthetic and real datasets.
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/.