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 paramet...
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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 emalgorithm) 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 emalgorithm: 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.
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