Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Interpretations of various uncertainty theories using models of modal logic: a summary
Fuzzy Sets and Systems
Some mathematical aspects of fuzzy sets: triangular norms, fuzzy logics, and generalized measures
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Mining fuzzy association rules in databases
ACM SIGMOD Record
Constructing fuzzy measures in expert systems
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Fuzzy Sets and Systems
Learning from data: possibilistic graphical models
Handbook of defeasible reasoning and uncertainty management systems
Canonical forms of fuzzy truthoods by meta-theory based upon modal logic
Information Sciences: an International Journal
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
A context model for fuzzy concept analysis based upon modal logic
Information Sciences—Informatics and Computer Science: An International Journal
Fuzziness and uncertainty within the framework of context model
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Interpreting and extracting fuzzy decision rules from fuzzy information systems and their inference
Information Sciences: an International Journal
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In this paper we show that the context model proposed by Gebhardt and Kruse (1993) can be semantically extended and considered as a data model for constructing membership functions of fuzzy concepts within the framework of meta-theory developed by Resconi et al. in 1990s. Within this framework, we integrate context models by using a model of modal logic, and develop a method for calculating the expressions for the membership functions of composed fuzzy concepts based on values {0, 1}, which correspond to the truth values {F, T} assigned to a given sentence as the response of a context considered as a possible world. It is of interest that fuzzy intersection and fuzzy union operators by this model are truth-functional, and, moreover, they form a well-known dual pair of Product t-norm TP and Probabilistic Sum t-conorm SP.