TSRuleGrowth: Mining partially-ordered prediction rules from a time series of discrete elements, application to a context of ambient intelligence

Benoit Vuillemin 1, 2 Lionel Delphin-Poulat 3 Rozenn Nicol 1 Laëtitia Matignon 2 Salima Hassas 2
1 Orange Labs [Lannion]
2 SMA - Systèmes Multi-Agents
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 FTR&D - France Télécom Recherche et Développement [Lannion]
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Conference papers
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https://hal.archives-ouvertes.fr/hal-02305993
Contributor : Benoit Vuillemin <>
Submitted on : Friday, October 4, 2019 - 5:04:01 PM
Last modification on : Wednesday, November 20, 2019 - 3:19:32 AM

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  • HAL Id : hal-02305993, version 1

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EC-LYON | CNRS | UNIV-LYON1 | INSA-LYON | LIRIS | UNIV-LYON2 | INSA-GROUPE | UDL

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Benoit Vuillemin, Lionel Delphin-Poulat, Rozenn Nicol, Laëtitia Matignon, Salima Hassas. TSRuleGrowth: Mining partially-ordered prediction rules from a time series of discrete elements, application to a context of ambient intelligence. ADMA: Advanced Data Mining and Applications, Nov 2019, Dalian, China. ⟨hal-02305993⟩

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