Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system
Information Sciences: an International Journal
Formal concept analysis based on fuzzy granularity base for different granulations
Fuzzy Sets and Systems
Information granularity and granular structure in decision making
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Rough set model based on formal concept analysis
Information Sciences: an International Journal
Entropy measures and granularity measures for set-valued information systems
Information Sciences: an International Journal
Set-based granular computing: A lattice model
International Journal of Approximate Reasoning
On Characterizing Hierarchies of Granulation Structures via Distances
Fundamenta Informaticae
Uncertainty measure of Atanassov's intuitionistic fuzzy T equivalence information systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Zadeh’s seminal work in theory of fuzzy-information granulation in human reasoning is inspired by the ways in which humans granulate information and reason with it. This has led to an interesting research topic: granular computing (GrC). Although many excellent research contributions have been made, there remains an important issue to be addressed: What is the essence of measuring a fuzzy-information granularity of a fuzzy-granular structure? What is needed to answer this question is an axiomatic constraint with a partial-order relation that is defined in terms of the size of each fuzzy-information granule from a fuzzy-binary granular structure. This viewpoint is demonstrated for fuzzy-binary granular structure, which is called the binary GrC model by Lin. We study this viewpoint from from five aspects in this study, which are fuzzy BINARY-granular-structure operators, partial-order relations, measures for fuzzy-information granularity, an axiomatic approach to fuzzy-information granularity, and fuzzy-information entropies.