Flexible mixture models for automatic clustering
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Wallace, C. S. & Dowe, D. L. MML clustering of multi-state, Poisson, von Mises circular and Gaussian distributions. Statistics and Computing, 2000 , 10, pp. 73-83
Wallace, C. S. Intrinsic Classification of Spatially Correlated Data. The Computer Journal, 1998, 41, pp. 602-611
Wallace, C. S. Statistical and Inductive Inference by Minimum Message Length. Springer, 2005
Schmidt, D. F. & Makalic, E. Minimum Message Length Inference and Mixture Modelling of Inverse Gaussian Distributions. AI 2012: Advances in Artificial Intelligence, Springer Berlin Heidelberg, 2012, 7691, pp. 672-682
Edwards, R. T. & Dowe, D. L. Single factor analysis in MML mixture modelling. Research and Development in Knowledge Discovery and Data Mining, Second Pacific-Asia Conference (PAKDD-98), 1998, 1394
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Versión | Publicado | Notas de la versión | |
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0.81 | -added function minmis() to compute the minimum number of misclassifications by label rotation
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0.80 | -added mixtures of principal component analyzers and a new example |
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0.75 | -added Pareto (Type II) mixture models
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0.70 | -added mixtures of Dirichlet distributions and a new example |
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0.65 | -added mixtures of exponential and Weibull models with type I (right) censoring
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0.60 | -added the lognormal distribution
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0.50 | -added mixture models for censored exponential and Weibull distributions
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0.40 | -added beta and von Mises Fisher distributions
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0.30 | -significant speed improvement in gamma, Laplace mixture models
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0.2.2 | -Added mixtures of Laplace distributions
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0.2.1 | -Minor title change
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0.2.0 |