Bayesian Network Structure Learning: The Two-Step Clustering-Based Algorithm

In this paper we introduce a two-step clustering-based strategy, which can automatically generate prior information from data in order to further improve the accuracy and time ef- ficiency of state-of-the-art algorithms for Bayesian network structure learning. Our clustering-based strategy is composed of two steps. In the first step, we divide the potential nodes into several groups via clustering analysis and apply Bayesian network structure learning to obtain some pre-existing arcs within each cluster. In the second step, with all the withincluster arcs being well preserved, we learn the betweencluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms benefit from the proposed clusteringbased strategy in terms of both accuracy and efficiency.

Authors

• Yikun Zhang
• Jiming Liu
• Yang Liu College of Computer Science, Sichuan University Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education

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