TSRuleGrowth : Extraction de r\`egles de pr\'ediction semi-ordonn\'ees
  \`a partir d'une s\'erie temporelle d'\'el\'ements discrets, application dans
  un contexte d'intelligence ambiante
  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.
