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Author (up) Pham, D.T.; Ruz, G.A. pdf  doi
  Title Unsupervised training of Bayesian networks for data clustering Type Journal Article
  Year 2009 Publication Proceedings Of The Royal Society A-Mathematical Physical And Engineering Sciences Abbreviated Journal Proc. R. Soc. A-Math. Phys. Eng. Sci.  
  Volume 465 Issue 2109 Pages 2927-2948  
  Keywords Bayesian networks; clustering; unsupervised training; classification expectation-maximization algorithm; machine learning  
  Abstract This paper presents a new approach to the unsupervised training of Bayesian network classifiers. Three models have been analysed: the Chow and Liu (CL) multinets; the tree-augmented naive Bayes; and a new model called the simple Bayesian network classifier, which is more robust in its structure learning. To perform the unsupervised training of these models, the classification maximum likelihood criterion is used. The maximization of this criterion is derived for each model under the classification expectation-maximization ( EM) algorithm framework. To test the proposed unsupervised training approach, 10 well-known benchmark datasets have been used to measure their clustering performance. Also, for comparison, the results for the k-means and the EM algorithm, as well as those obtained when the three Bayesian network classifiers are trained in a supervised way, are analysed. A real-world image processing application is also presented, dealing with clustering of wood board images described by 165 attributes. Results show that the proposed learning method, in general, outperforms traditional clustering algorithms and, in the wood board image application, the CL multinets obtained a 12 per cent increase, on average, in clustering accuracy when compared with the k-means method and a 7 per cent increase, on average, when compared with the EM algorithm.  
  Address [Pham, Duc Truong; Ruz, Gonzalo A.] Cardiff Univ, Mfg Engn Ctr, Cardiff CF24 3AA, S Glam, Wales, Email:  
  Corporate Author Thesis  
  Publisher Royal Soc Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1364-5021 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000268317700016 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 62  
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