Software modules categorization through likelihood and bayesian analysis of finite dirichlet mixtures

In this paper, we examine deterministic and Bayesian methods for analyzing finite Dirichlet mixtures. The deterministic method is based on the likelihood approach, and the Bayesian approach is implemented using the Gibbs sampler. The selection of the number of clusters for both approaches is based on the Bayesian information criterion, which is equivalent to the minimum description length. Experimental results are presented using simulated data, and a real application for software modules classification is also included.