Extracting Decision Trees from Interval Pattern Concept Lattices

Zainab Assaghir; Mehdi Kaytoue; Wagner Meira; Jean Villerd

Extracting Decision Trees from Interval Pattern Concept Lattices

Zainab Assaghir ¹ Mehdi Kaytoue ² Wagner Meira ² Jean Villerd ^{3, *}

* Corresponding author

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Machine Learning [cs.LG]

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https://hal.inria.fr/hal-00640938
Contributor : Mehdi Kaytoue <>
Submitted on : Monday, November 14, 2011 - 4:29:14 PM
Last modification on : Thursday, January 11, 2018 - 6:19:54 AM
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