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

Authors

  • Yikun Zhang Sun Yat-sen University
  • Jiming Liu Hong Kong Baptist University
  • Yang Liu Hong Kong Baptist University

Keywords:

Bayesian Networks, Clustering Analysis, Two-step Structure Learning

Abstract

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 efficiency 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 within-cluster arcs being well preserved, we learn the between-cluster structure of the given network. Experimental results on benchmark datasets show that a wide range of structure learning algorithms benefit from the proposed clustering-based strategy in terms of both accuracy and efficiency.

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Published

2018-04-29

How to Cite

Zhang, Y., Liu, J., & Liu, Y. (2018). Bayesian Network Structure Learning: The Two-Step Clustering-Based Algorithm. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12129