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Journal of Applied Mathematics
Volume 2012, Article ID 974063, 17 pages
Research Article

A Decomposition Algorithm for Learning Bayesian Networks Based on Scoring Function

Department of Mathematics, Xidian University, Xi'an 710071, China

Received 14 May 2012; Revised 12 August 2012; Accepted 28 August 2012

Academic Editor: B. V. Rathish Kumar

Copyright © 2012 Mingmin Zhu and Sanyang Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Learning Bayesian network (BN) structure from data is a typical NP-hard problem. But almost existing algorithms have the very high complexity when the number of variables is large. In order to solve this problem(s), we present an algorithm that integrates with a decomposition-based approach and a scoring-function-based approach for learning BN structures. Firstly, the proposed algorithm decomposes the moral graph of BN into its maximal prime subgraphs. Then it orientates the local edges in each subgraph by the K2-scoring greedy searching. The last step is combining directed subgraphs to obtain final BN structure. The theoretical and experimental results show that our algorithm can efficiently and accurately identify complex network structures from small data set.