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This paper proposes a method of segmenting temporal data into orderedclasses. It is based on mixture models and a discrete latent process, whichenables to successively activates the classes. The classification can beperformed by maximizing the likelihood via the EM algorithm or bysimultaneously optimizing the model parameters and the partition by the CEMalgorithm. These two algorithms can be seen as alternatives to Fisher'salgorithm, which improve its computing time.

Classification automatique de donn\'ees temporelles en classes ordonn\'ees