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Genetic Programming for Optimizing Fuzzy Gradual Pattern Discovery

Sarra Ayouni 1 Sadok Ben Yahia 1 Anne Laurent 2 Pascal Poncelet 2 
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Gradual patterns refer to frequent patterns describing correlations between variables which evolution is linked. For instance, the gradual pattern the older, the higher means that the age and the salary increase/decrease simultaneously between two persons. This co-evolution can be found either as increasing together or evolving oppositely (e.g., the more cars, the less bus tickets). Several approaches were proposed to mine such patterns. These approaches differ depending on the way they count how frequent a pattern is, or depending on their efficiency both for memory and time consumption. The approaches can also differ depending on the way the attributes are treated, i.e. if they are considered as monotonically growing within the range of values or if they are considered as a fuzzy partition. For instance, the pattern the closer the age of an employee to 46, the higher his/her income is called to be a fuzzy gradual pattern. The challenge is then to retrieve the fuzzy sets (e.g. almost 46) that allow to mine the most relevant fuzzy gradual patterns. In this paper we focus on how genetic programming can be used in this context.
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Submitted on : Monday, March 25, 2019 - 11:29:54 AM
Last modification on : Friday, August 5, 2022 - 10:46:40 AM
Long-term archiving on: : Wednesday, June 26, 2019 - 1:59:36 PM


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  • HAL Id : lirmm-00825334, version 1



Sarra Ayouni, Sadok Ben Yahia, Anne Laurent, Pascal Poncelet. Genetic Programming for Optimizing Fuzzy Gradual Pattern Discovery. EUSFLAT-LFA, European Society for Fuzzy Logic and Technology, Jul 2011, Aix les Bains, France. pp.305-310. ⟨lirmm-00825334⟩



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