In this article, we integrate Relational Concept Analysis with fuzzy logic to explore multi-relational datasets including also vague information. Mainly, we aim to extract a family of fuzzy concept lattices from data organized as a collection of fuzzy formal contexts and fuzzy relations between objects of different types. To achieve this goal, we use existing fuzzy FCA techniques and fuzzy scaling quantifiers. Our principal contribution here consists of introducing and studying fuzzy scaling quantifiers, which are fuzzy quantifiers based on the concept of evaluative linguistic expression.

Boffa, S., Murinova, P., Novak, V. (2021). A proposal to extend Relational Concept Analysis with fuzzy scaling quantifiers [Formula presented]. KNOWLEDGE-BASED SYSTEMS, 231 [10.1016/j.knosys.2021.107452].

A proposal to extend Relational Concept Analysis with fuzzy scaling quantifiers [Formula presented]

Boffa S;
2021

Abstract

In this article, we integrate Relational Concept Analysis with fuzzy logic to explore multi-relational datasets including also vague information. Mainly, we aim to extract a family of fuzzy concept lattices from data organized as a collection of fuzzy formal contexts and fuzzy relations between objects of different types. To achieve this goal, we use existing fuzzy FCA techniques and fuzzy scaling quantifiers. Our principal contribution here consists of introducing and studying fuzzy scaling quantifiers, which are fuzzy quantifiers based on the concept of evaluative linguistic expression.
Articolo in rivista - Articolo scientifico
Fuzzy formal concept analysis; Fuzzy quantifiers; Relational concept analysis; Scaling quantifiers
English
2021
231
107452
reserved
Boffa, S., Murinova, P., Novak, V. (2021). A proposal to extend Relational Concept Analysis with fuzzy scaling quantifiers [Formula presented]. KNOWLEDGE-BASED SYSTEMS, 231 [10.1016/j.knosys.2021.107452].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/395854
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