Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Concept lattices defined from implication operators
Fuzzy Sets and Systems
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
Using a Concept Lattice of Decomposition Slices for Program Understanding and Impact Analysis
IEEE Transactions on Software Engineering
Concept analysis via rough set and AFS algebra
Information Sciences: an International Journal
Interpretability constraints for fuzzy information granulation
Information Sciences: an International Journal
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Multi-adjoint t-concept lattices
Information Sciences: an International Journal
Editorial: Introduction to special issues on data mining and granular computing
International Journal of Approximate Reasoning
Axiomatic approach of knowledge granulation in information system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Toward a Theory of Granular Computing for Human-Centered Information Processing
IEEE Transactions on Fuzzy Systems
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Axiomatic characterizations of dual concept lattices
International Journal of Approximate Reasoning
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
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In this paper, a novel cognitive system model is established based on formal concept analysis to exactly describe human cognitive processes. Two new operators, extent-intent and intent-extent, are introduced between an object and its attributes. By analyzing the necessity and sufficient relations between the object and some of its attributes, the information granule concept is investigated in human cognitive processes. Furthermore, theories of transforming arbitrary information granule into necessary, sufficient, sufficient and necessary information granules are addressed carefully. Algorithm of the transformation is constructed, by which we can provide an efficient approach to the conversion among information granules. To interpret and help understand the theories and algorithm, an experimental computing program is designed and two cases is employed as case study. Results of the small scale case are calculated by the method presented in this paper. The large-scale case is calculated by the experimental computing program and validated by the proposed algorithm. The considered framework can provide a novel convenient tool for artificial intelligence researches.