Discovering knowledge from fuzzy concept lattice
Data mining and computational intelligence
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Fuzzy Relational Systems: Foundations and Principles
Fuzzy Relational Systems: Foundations and Principles
A multi-level conceptual data reduction approach based on the Lukasiewicz implication
Information Sciences: an International Journal - Special issue: Information technology
Concept lattices and similarity in non-commutative fuzzy logic
Fundamenta Informaticae
Thresholds and shifted attributes in formal concept analysis of data with fuzzy attributes
ICCS'06 Proceedings of the 14th international conference on Conceptual Structures: inspiration and Application
Crisply generated fuzzy concepts
ICFCA'05 Proceedings of the Third international conference on Formal Concept Analysis
Certificateless threshold cryptosystem secure against chosen-ciphertext attack
Information Sciences: an International Journal
Grouping fuzzy sets by similarity
Information Sciences: an International Journal
Multi-adjoint t-concept lattices
Information Sciences: an International Journal
A heuristic knowledge-reduction method for decision formal contexts
Computers & Mathematics with Applications
Extending conceptualisation modes for generalised Formal Concept Analysis
Information Sciences: an International Journal
Evaluation of IPAQ questionnaires supported by formal concept analysis
Information Sciences: an International Journal
Note on generating fuzzy concept lattices via Galois connections
Information Sciences: an International Journal
Formal concept analysis based on fuzzy granularity base for different granulations
Fuzzy Sets and Systems
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The present paper deals with formal concept analysis of data with fuzzy attributes. We clarify several points of a new approach of [S.Q. Fan, W.X. Zhang, Variable threshold concept lattice, Inf. Sci., accepted for publication] which is based on using thresholds in concept-forming operators. We show that the extent- and intent-forming operators from [S.Q. Fan, W.X. Zhang, Inf. Sci., accepted for publication] can be defined in terms of basic fuzzy set operations and the original operators as introduced and studied e.g. in [R. Belohlavek, Fuzzy Galois connections, Math. Logic Quarterly 45 (4) (1999) 497-504; R. Belohlavek, Concept lattices and order in fuzzy logic, Ann. Pure Appl. Logic 128 (2004) 277-298; S. Pollandt, Fuzzy Begriffe, Springer-Verlag, Berlin/Heidelberg, 1997]. As a consequence, main properties of the new operators from [S.Q. Fan, W.X. Zhang, Inf. Sci., accepted for publication], including the properties studied in [S.Q. Fan, W.X. Zhang, Inf. Sci., accepted for publication], can be obtained as consequences of the original operators from [R. Belohlavek, 1999; R. Belohlavek, 2004; S. Pollandt, 1997].