A Method for Hybrid Bayesian Network Structure Learning from Massive Data Using MapReduce

Bayesian Network is the popular and important data mining model for representing uncertain knowledge. For large scale data it is often too costly to learn the accurate structure. To resolve this problem, much work has been done on migrating the structure learning algorithms to the MapReduce framework. In this paper, we introduce a distributed hybrid structure learning algorithm by combining the advantages of constraint-based and score-and-search-based algorithms. By reusing the intermediate results of MapReduce, the algorithm greatly simplified the computing work and got good results in both efficiency and accuracy.