International Association of Computer Science and Information Technology(IACSIT)
A Bayesian Network (BN) is an appropriate tool to work with the uncertainty that is typical of real-life applications. Basically, a BN provides an effective graphical language for factoring joint probability distributions. Two important methods of learning bayesian are parametric learning and structural learning. Finding bayesian network structure is a NP-hard problem. In this paper, the authors introduced structural learning in bayesian network and key learning algorithms, like Hill Climbing and K2 and briefly. As a second step, they presented a new structural learning method using composite of k2 search algorithm and single link clustering method.