Rough set theory is a tool for studying imprecision, vagueness, and uncertainty in data analysis. A rough set is an approximation of a vague concept by a pair of precise concepts, called lower and upper approximations (which are a classification of the domain of interest into disjoint categories). The classification formally represents knowledge about the problem domain.
I'll be explaining the concepts involved and practical applications, particularly focussing on rough set attribute reduction for the reduction of dataset dimensionality.