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Knowledge Acquisition under Imprecision through Neighborhood Approximation Operators
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 29 - Number 10 |
| Year of Publication: 2011 |
| Authors: Dr.D.Mohanty, Dr.J.K.Mantri, Dr.N.Kalia, B.B.Nayak |
10.5120/3602-5005
|
Dr.D.Mohanty, Dr.J.K.Mantri, Dr.N.Kalia, B.B.Nayak . Knowledge Acquisition under Imprecision through Neighborhood Approximation Operators. International Journal of Computer Applications. 29, 10 ( September 2011), 1-10. DOI=10.5120/3602-5005
Abstract
The notion of rough sets, introduced by Z. Pawlak in 1982, is to capture impreciseness and indiscernibility of objects. The basic assumption of rough set theory is that human knowledge about a universe depends upon their capability to classify its objects. Classifications (or partitions) of a universe and equivalence relations defined on it are known to be interchangeable notions. So, for mathematical reasons, equivalence relations were considered by Pawlak to define rough sets. But in practice, we can get non-equivalence relations, rather than equivalence relations for the study of approximations. In this paper, we find notion of neighborhood systems instead of equivalence relations, proposed by Lin (1988), Chu (1992) and Lin & Yao (1996), for the study of approximation and also we study some properties of 1-neighborhood systems.
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