Extracting Decision Trees from Interval Pattern Concept Lattices

Zainab Assaghir 1 Mehdi Kaytoue 2 Wagner Meira 2 Jean Villerd 3, *
* Auteur correspondant
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
2 DCC - UFMG - Departamento de Ciência da Computação [Minas Gerais]
3 ENSAIA - Ecole Nationale Supérieure en Agronomie et Industries Alimentaires
Abstract : Formal Concept Analysis (FCA) and concept lattices have shown their e ffectiveness for binary clustering and concept learning. Moreover, several links between FCA and unsupervised data mining tasks such as itemset mining and association rules extraction have been emphasized. Several works also studied FCA in a supervised framework, showing that popular machine learning tools such as decision trees can be extracted from concept lattices. In this paper, we investigate the links between FCA and decision trees with numerical data. Recent works showed the effciency of "pattern structures" to handle numerical data in FCA, compared to traditional discretization methods such as conceptual scaling.
Keywords : decision trees formal concept analysis pattern structures numerical data
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Communication dans un congrès
Amedeo Napoli and Vilem Vychodil. The Eighth International Conference on Concept Lattices and their Applications - CLA 2011, Oct 2011, Nancy, France. 2011
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Dernière modification le : jeudi 11 janvier 2018 - 06:19:54
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Zainab Assaghir, Mehdi Kaytoue, Wagner Meira, Jean Villerd. Extracting Decision Trees from Interval Pattern Concept Lattices. Amedeo Napoli and Vilem Vychodil. The Eighth International Conference on Concept Lattices and their Applications - CLA 2011, Oct 2011, Nancy, France. 2011. 〈hal-00640938〉

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