Lundi 9 Mars

Heure:	14:00 - 15:00
Lieu:	Salle B107, bâtiment B, Université de Villetaneuse
Résumé:	Sequence Classification Based on Delta-Free Sequential Patterns
Description:	Pierre Holat Sequential pattern mining is one of the most studied and challenging tasks in data mining. However, the extension of well-known methods from many other classical patterns to sequences is not a trivial task. This talk presents a study of the notion of delta-freeness for sequences. While this notion has extensively been discussed for itemsets, the work described in this talk is the first to extend it to sequences. In this work, we define an efficient algorithm devoted to the extraction of delta-free sequential patterns. Furthermore, we show the advantage of the delta-free sequences and highlight their importance when building sequence classifiers, and we show how they can be used to address the feature selection problem in statistical classifiers, as well as to build symbolic classifiers which optimizes both accuracy and earliness of predictions.