Representations of Vague Approximation Operators


Article Preview

As a suitable mathematical model to handle partial knowledge in data bases, rough set theory is emerging as a powerful theory and has been found its successive applications in the fields of artificial intelligence such as pattern recognition, machine learning, etc. In the paper, a vague relation is first defined, which is the extension of fuzzy relation. Then a new pair of lower and upper generalized rough approximation operators based on the vague relation is first proposed by us. Finally, the representations of vague rough approximation operators are presented.



Advanced Materials Research (Volumes 282-283)

Edited by:

Helen Zhang and David Jin




H. D. Zhang and Y. P. He, "Representations of Vague Approximation Operators", Advanced Materials Research, Vols. 282-283, pp. 287-290, 2011

Online since:

July 2011




[1] Z. Pawlak: Rough sets. Internal Joural of Computer and Information Science, Vol. 11(1982), pp.341-356.

[2] Z. Pawlak, Rough sets: Theoretical Aspects of Reasoning about Data, Boston: Kluwer Academic Publishers, (1991).

[3] Gau W L, Buehrer D J: Vague sets. IEEE Trans Systems Man Cybernetics, Vol. 23 (1993), pp.610-614.


[4] D. Dubois, H. Prade: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems, Vol. 17(1990), pp.191-208.


[5] Yan D Q, Chi X Z: Decomposition theoren and measure of similarity in vague sets. Computer Science, Vol. 30(2003), pp.78-79.

[6] Lei Z, Wu W Z: On generalized intuitionistic fuzzy rough approximation operators. Information Sciences, Vol. 178(2008), pp.2448-2465.