A Parameterized Algorithm for Exploring Concept Lattices - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2007

A Parameterized Algorithm for Exploring Concept Lattices

Peggy Cellier
Sébastien Ferré
Olivier Ridoux
Mireille Ducassé

Résumé

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.
Fichier principal
Vignette du fichier
icfca07.pdf (182.63 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-00180601 , version 1 (19-10-2007)

Identifiants

  • HAL Id : hal-00180601 , version 1

Citer

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⟩
374 Consultations
124 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More