iMMPC: A Local Search Approach for Incremental Bayesian Network Structure Learning

The dynamic nature of data streams leads to a number of computational and mining challenges. In such environments, Bayesian network structure learning incrementally by revising existing structure could be an efficient way to save time and memory constraints. The local search methods for structure learning outperforms to deal with high dimensional domains. The major task in local search methods is to identify the local structure around the target variable i.e. parent children (PC). In this paper we transformed the local structure identification part of MMHC algorithm into an incremental fashion by using heuristics proposed by reducing the search space. We applied incremental hill-climbing to learn a set of candidate- parent-children (CPC) for a target variable. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.