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International audience; The problem of mining for outliers in sequential datasets is crucial to forward appropriate analysis of data. Therefore, many approaches for the discovery of such anomalies have been proposed. However, most of them use a sample of known typical sequences to build the model. Besides, they remain greedy in terms of memory usage. In this paper we propose an extension of one such approach, based on a Probabilistic Suffix Tree and on a measure of similarity. We add a pruning criterion which reduces the size of the tree while improving the model, and a sharp inequality for the concentration of the measure of similarity, to better sort the outliers. We prove the feasability of our approach through a set of experiments over a protein database.

The discovery of frequent sequential patterns in an ordered collection of data, such as sequential databases or time-series data, is an important issue in several contexts. In this paper, we employ formal concept analysis to develop the notion of closure for these sequential patterns and to characterize the concept lattice of the ordered contexts. The proposed concept lattice will serve as a model for the patterns extracted in the context of sequential databases by a recent algorithm (CloSpan, cite{Clospan}). Finally, we will show how we can also use our model to derive other kind of structured patterns, like the closed set of episodes in the context of time-series data cite{Toivonen}. So, the convenient transformation of the sequential patterns in the concepts of the lattice will give rise to the most representative set of parallel and serial closed episodes.