Classification of binary vectors by using ΔSC distance to minimize stochastic complexity
Pattern Recognition Letters
Compact and understandable descriptions of mixtures of Bernoulli distributions
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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We give a survey of different probabilistic partitioning methods that have been applied to bacterial taxonomy. We introduce a theoretical framework, which makes it possible to treat the various models in a unified way. The key concepts of our approach are prediction and storing of microbiological information in a Bayesian forecasting setting. We show that there is a close connection between classification and probabilistic identification and that, in fact, our approach ties these two concepts together in a coherent way.