AFS-Based Formal Concept Analysis within the Logic Description of Granules
AFS (Axiomatic Fuzzy Sets) -based formal concept is a generalization and development of classical concept lattice and monotone concept, which can be applied to represent the logic operations of queries in information retrieval. Granular computing is an emerging field of study that attempts to formalize and explore methods and heuristics of human problem solving with multiple levels of granularity and abstraction. The main objective of this paper is to investigate and develop AFS-based formal concept by using granule logics. Some generalized formulas of granular computing are introduced, in which AFS-based formal concept and AFS-based formal concept on multi-valued context are interpreted from the point of granular computing, respectively.
KeywordsFormal concept concept lattice granular computing AFS-based formal concept
Unable to display preview. Download preview PDF.
- 4.Lin, T.Y.: Granular Computing. Announcement of the BISC Special Interest Group on Granular Computing (1997)Google Scholar
- 14.Wille, R.: Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel (1982)Google Scholar
- 16.Yao, J.T.: A Ten-Year Review of Granular Computing. In: GrC 2007, pp. 734–739 (2007)Google Scholar
- 17.Yao, Y.Y.: Granular Computing: Basic Issues and Possible Solutions. In: Proceedings of the 5th Joint Conference on Information Sciences, pp. 186–189 (2000)Google Scholar
- 19.Yao, Y.Y., Zhou, B.: A Logic Language of Granular Computing. In: 6th IEEE International Conference on Cognitive Informatics, pp. 178–185 (2007)Google Scholar
- 20.Zadeh, L.A.: Fuzzy Sets and Information Granurity. In: Gupta, M., Ragade, R.K., Yager, R.R. (eds.) Advances in Fuzzy Set Theory and Applications, pp. 3–18. North-Holland Publishing Company (1979)Google Scholar
- 21.Zadeh, L.A.: Key Roles of Information Granulation and Fuzzy Logic in Human Reasoning, Concept Formulation and Computing with Words. In: Proceedings of IEEE 5th International Fuzzy Systems, p. 1 (1996)Google Scholar
- 24.Zhao, Y., Halang, W.A., Wang, X.: Rough Ontology Mapping in E-Business Integration. SCI, vol. 37, pp. 75–93. Springer, Heidelberg (2007)Google Scholar