Data clustering using variational learning of finite scaled dirichlet mixture models with component splitting

Abstract

We have developed a variational learning approach for finite Scaled Dirichlet mixture model with local model selection framework. By gradually splitting the components, our model is able to reach convergence as well as obtain the optimal number of clusters. By tackling real life challenging problems including spam detection and object clustering, the proposed model’s flexibility and performance are validated.