TSRuleGrowth : Extraction de règles de prédiction semi-ordonnées à partir d'une série temporelle d'éléments discrets, application dans un contexte d'intelligence ambiante

Benoit Vuillemin, Lionel Delphin-Poulat, Rozenn Nicol, Laëtitia Matignon, Salima Hassas

Abstract: This paper presents a new algorithm: TSRuleGrowth, looking for partially-ordered rules over a time series. This algorithm takes principles from the state of the art of rule mining and applies them to time series via a new notion of support. We apply this algorithm to real data from a connected environment, which extract user habits through different connected objects.