A Parameterized Algorithm for Exploring Concept LatticesReport as inadecuate




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1 LIS - Logical Information Systems IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE

Abstract : Formal Concept Analysis FCA is a natural framework for learning from positive and negative examples. Indeed, learning from ex- amples results in sets of frequent concepts whose extent contains only these examples. In terms of association rules, the above learning strat- egy can be seen as searching the premises of exact rules where the conse- quence is fixed. In its most classical setting, FCA considers attributes as a non-ordered set. When attributes of the context are ordered, Conceptual Scaling allows the related taxonomy to be taken into account by produc- ing a context completed with all attributes deduced from the taxonomy. The drawback, however, is that concept intents contain redundant in- formation. In this article, we propose a parameterized generalization of a previously proposed algorithm, in order to learn rules in the presence of a taxonomy. The taxonomy is taken into account during the compu- tation so as to remove all redundancies from intents. Simply changing one component, this parameterized algorithm can compute various kinds of concept-based rules. We present instantiations of the parameterized algorithm for learning positive and negative rules.

Keywords : concept analysis algorithm taxonomy





Author: Peggy Cellier - Sébastien Ferré - Olivier Ridoux - Mireille Ducassé -

Source: https://hal.archives-ouvertes.fr/



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