Model-based Clustering with Noise: Bayesian Inference and Estimation

This paper proposes a new way of overcoming the existing limitations. It generalizes the model used in the previous approaches by introducing a more comprehensive portfolio of covariance matrix structures. Further, this paper proposes a Bayesian solution in the presence of the noise in clustering problems. The performace of the proposed method is first studied by simulation; the procedure is also applied to the analysis of data concerning species of butterflies and diabetes patients.