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TSRuleGrowth: Mining Partially-Ordered Prediction Rules From a Time Series of Discrete Elements, Application to a Context of Ambient Intelligence

  • Benoit VuilleminEmail author
  • Lionel Delphin-Poulat
  • Rozenn Nicol
  • Laetitia Matignon
  • Salima Hassas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)

Abstract

This paper presents TSRuleGrowth, an algorithm for mining partially-ordered rules on a time series. TSRuleGrowth takes principles from the state of the art of transactional rule mining, and applies them to time series. It proposes a new definition of the support, which overcomes the limitations of previous definitions. Experiments on two databases of real data coming from connected environments show that this algorithm extracts relevant usual situations and outperforms the state of the art.

Keywords

Rule mining Ambient intelligence Habits Automation Support Time series 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Benoit Vuillemin
    • 1
    • 2
    Email author
  • Lionel Delphin-Poulat
    • 1
  • Rozenn Nicol
    • 1
  • Laetitia Matignon
    • 2
  • Salima Hassas
    • 2
  1. 1.Orange LabsLannionFrance
  2. 2.Univ Lyon, Université Lyon 1, CNRS, LIRIS, UMR5205LyonFrance

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