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A Parameterized Algorithm for Exploring Concept Lattices

Peggy Cellier 1 Sébastien Ferré 1 Olivier Ridoux 1 Mireille Ducassé 1
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.
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  • HAL Id : hal-00180601, version 1

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Peggy Cellier, Sébastien Ferré, Olivier Ridoux, Mireille Ducassé. A Parameterized Algorithm for Exploring Concept Lattices. Int. Conf. Formal Concept Analysis, Feb 2007, France. pp.114--129. ⟨hal-00180601⟩

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