TY - GEN
T1 - Fuzzy concept lattices determined by (θ,σ)-fuzzy rough approximation operators
AU - Yao, Yan Qing
AU - Mi, Ju Sheng
PY - 2009
Y1 - 2009
N2 - Formal concept analysis and rough set analysis are two complementary approaches for analyzing data. This paper studies approaches to constructing fuzzy concept lattices based on generalized fuzzy rough approximation operators. For a Lukasiewicz implicator θ and its dual σ, a pair of (θ,σ)-fuzzy rough approximation operators is defined. We then propose three kinds of fuzzy Galois connections, and examine some of their basic properties. Thus, three complete fuzzy concept lattices can be produced, for which the properties are analogous to those of the classical concept lattices.
AB - Formal concept analysis and rough set analysis are two complementary approaches for analyzing data. This paper studies approaches to constructing fuzzy concept lattices based on generalized fuzzy rough approximation operators. For a Lukasiewicz implicator θ and its dual σ, a pair of (θ,σ)-fuzzy rough approximation operators is defined. We then propose three kinds of fuzzy Galois connections, and examine some of their basic properties. Thus, three complete fuzzy concept lattices can be produced, for which the properties are analogous to those of the classical concept lattices.
KW - (θand σ)-fuzzy rough sets
KW - Approximation operators
KW - Fuzzy concept lattices
KW - Lukasiewicz implicator
UR - https://www.scopus.com/pages/publications/69049092078
U2 - 10.1007/978-3-642-02962-2_76
DO - 10.1007/978-3-642-02962-2_76
M3 - 会议稿件
AN - SCOPUS:69049092078
SN - 3642029612
SN - 9783642029615
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 601
EP - 609
BT - Rough Sets and Knowledge Technology - 4th International Conference, RSKT 2009, Proceedings
T2 - 4th International Conference on Rough Sets and Knowledge Technology, RSKT 2009
Y2 - 14 July 2009 through 16 July 2009
ER -