Hierarchical clustering based structural learning of Bayesian networks

Bayesian networks are being used in various domains, such as data mining, diagnosis, bioinformatics/computational biology, etc. One problem associated with Bayesian networks is to learn their structures from training data. In this paper, we introduce a new approach to structural learning of Bayesian networks, based on hierarchical clustering. We learn the network in hierarchical stages, learning over a subset of the random variables at each stage. Experiments show that this approach learns Bayesian networks faster as compared to curriculum-based learning methods. We show a comparison of our networks with curriculum based learned Bayesian networks over different evaluation metrics as well. Also, performance of hierarchical clustering vs an existing ordering-based algorithm is observed