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A Hybrid Algorithm for Generating Formal Concepts and Building Concept Lattice Using NextClosure and Nourine Algorithms
Date de parution
2016-7-18
Résumé
Concept lattice produced from a set of formal concepts is
used for representing concept hierarchy and has many applications in
knowledge representation and data mining. Different algorithms have
been proposed in the past for efficiently generating formal concepts and
building concept lattices. In this paper we introduce the idea of combining
existing algorithms in FCA with the aim of benefiting from their
specific advantages. As an example, we propose a hybrid model that
utilizes the NextClosure (NC) algorithm for generating formal concepts
and parts of the Nourine algorithm for building concept lattices.We compare
the proposed hybrid model with two of its counterparts: pure NC
and pure Nourine. Our experiments show that the hybrid model always
outperforms pure NC and for very large datasets can surpass the pure
Nourine, as well.
used for representing concept hierarchy and has many applications in
knowledge representation and data mining. Different algorithms have
been proposed in the past for efficiently generating formal concepts and
building concept lattices. In this paper we introduce the idea of combining
existing algorithms in FCA with the aim of benefiting from their
specific advantages. As an example, we propose a hybrid model that
utilizes the NextClosure (NC) algorithm for generating formal concepts
and parts of the Nourine algorithm for building concept lattices.We compare
the proposed hybrid model with two of its counterparts: pure NC
and pure Nourine. Our experiments show that the hybrid model always
outperforms pure NC and for very large datasets can surpass the pure
Nourine, as well.
Notes
, 2016
Nom de l'événement
The International Workshop on Algorithms for FCA and Data Mining (AFCADM 2016)
Lieu
Moscow, Russia
Identifiants
Type de publication
conference paper
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