A tutorial on learning with Bayesian networks
Learning in graphical models
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Decomposition of search for v-structures in DAGs
Journal of Multivariate Analysis
Computational methods for Traditional Chinese Medicine: A survey
Computer Methods and Programs in Biomedicine
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
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Bayesian networks and cluster analysis are widely applied to network construction, data mining and causal discovery in bioinformation and medical researches. A Bayesian network is used to describe associations among a large number of variables, such as a gene network and a network describing relationships among symptoms. Cluster analysis is used to cluster associated variables, For example, genes with similar expressions or associated symptoms are grouped into a cluster. In this paper, we combine these approaches of network construction and cluster analysis together. On the one hand, we use Bayesian networks to explain relationships among variables in each cluster; on the other hand we use hierarchical cluster approach to assist network construction, and we propose a structure learning approach. In the stepwise approach, a subnetwork over a larger cluster is constructed by combining several subnetworks over small clusters whenever these small clusters are grouped together. The proposed approach is applied to a traditional Chinese medical study on a kidney disease.