Dependence space of concept lattices based on rough set

Rough set theory and Formal concept analysis have much in common, in terms of both goals and methodologies. The combination of rough set theory and formal concept analysis provides new approaches for data analysis. The notions of the object oriented concepts and the attribute oriented concepts are formed by introduced formal concept and formal concept lattice into rough set theory. In this paper, the dependence spaces are constructed according to these two concept lattices. Applying to the congruences on the dependence space, the equivalent classes of the set of attributes can be got and then a closed set is also obtained. And a new approach is discussed by using the closed set to construct formal concepts.

[1]  Ivo Düntsch,et al.  Modal-style operators in qualitative data analysis , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[2]  Wojciech Ziarko,et al.  Variable Precision Rough Set Model , 1993, J. Comput. Syst. Sci..

[3]  Zdzislaw Pawlak,et al.  Rough Set Theory and its Applications to Data Analysis , 1998, Cybern. Syst..

[4]  Yiyu Yao,et al.  Concept lattices in rough set theory , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[5]  Yiyu Yao,et al.  Rough set approximations in formal concept analysis , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[6]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[7]  Robert E. Kent,et al.  Rough Concept Analysis: A Synthesis of Rough Sets and Formal Concept Analysis , 1996, Fundam. Informaticae.

[8]  Miroslav Novotný,et al.  Dependence Spaces of Information Systems , 1998 .

[9]  Ming-Wen Shao,et al.  Approximation in Formal Concept Analysis , 2005, RSFDGrC.

[10]  Yiyu Yao,et al.  A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis , 2004, Rough Sets and Current Trends in Computing.

[11]  Marzena Kryszkiewicz,et al.  Rough Set Approach to Incomplete Information Systems , 1998, Inf. Sci..