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In order to cluster or partition data, we often useExpectation-and-Maximization (EM) or Variational approximation with a GaussianMixture Model (GMM), which is a parametric probability density functionrepresented as a weighted sum of $\hat{K}$ Gaussian component densities.However, model selection to find underlying $\hat{K}$ is one of the keyconcerns in GMM clustering, since we can obtain the desired clusters only when$\hat{K}$ is known. In this paper, we propose a new model selection algorithmto explore $\hat{K}$ in a Bayesian framework. The proposed algorithm builds thedensity of the model order which any information criterions such as AIC and BICbasically fail to reconstruct. In addition, this algorithm reconstructs thedensity quickly as compared to the time-consuming Monte Carlo simulation.

An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework