Abstract
Rough Set theory is useful for those applications into which data are uncertain, inconsistent, or redundant, and information searches for clarity. To discover a hidden pattern, especially in inconsistent data, rough set theory proved to be an efficient tool. Further, rough set theory has invincible role in decision making and granular computing. This paper reviews basic concepts on rough set theory with appropriate examples and extends discussing the concept of rough graph. The discussion on concepts has been extended with the inclusion of the uses and applications for knowledge generation.
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