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Fuzzy-Temporal Gradual Patterns

Dickson Owuor 1 Anne Laurent 1 Joseph Orero 2
1 FADO - Fuzziness, Alignments, Data & Ontologies
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Gradual patterns allow for retrieval of correlations between attributes through rules such as "the more the exercise, the less the stress". However, it may be the case that there is a lag between changes in some attributes and their impact on others ones, current methods do not take this into account. In this paper, we extend existing methods to handle these situations in order to retrieve patterns such as: "the more the exercise increases, the more the stress decreases 1 month later". We also extend our gradual rules to include fuzzy temporal constraints such as "the more the exercise increases, the more the stress decreases almost 1 month later". For this kinds of patterns, we designed three algorithms that were implemented and tested on real data.
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-02085779
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Submitted on : Wednesday, November 13, 2019 - 4:08:58 PM
Last modification on : Tuesday, July 21, 2020 - 6:28:02 PM
Long-term archiving on: : Friday, February 14, 2020 - 12:26:07 PM

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Dickson Owuor, Anne Laurent, Joseph Orero. Fuzzy-Temporal Gradual Patterns. International Conference on Fuzzy Systems (FUZZ-IEEE), Jun 2019, New Orleans, LA, United States. ⟨10.1109/FUZZ-IEEE.2019.8858883⟩. ⟨lirmm-02085779⟩

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