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An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering
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DOI：10.1016/S0167-8655(00)00038-6.png)
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Abstract
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The application of the Bayesian Structural EM algorithm to learn Bayesian networks (BNs) for clustering implies a search over the space of BN structures alternating between two steps: an optimization of the BN parameters (usually by means of the EM algorithm) and a structural search for model selection. In this paper, we propose to perform the optimization of the BN parameters using an alternative approach to the EM algorithm: the BC + EM method. We provide experimental results to show that our proposal results in a more effective and efficient version of the Bayesian Structural EM algorithm for learning BNs for clustering. (C) 2000 Elsevier Science B.V. All rights reserved.
Keywords:
clustering
Bayesian networks
EM algorithm
Bayesian structural EM algorithm
bound and collapse method
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