Logic-Based Roughification

The current chapter is devoted to roughification. In the most general setting, we intend the term roughification to refer to methods/techniques of constructing equivalence/similarity relations adequate for Pawlak-like approximations. Such techniques are fundamental in rough set theory. We propose and investigate novel roughification techniques. We show that using the proposed techniques one can often discern objects indiscernible by original similarity relations, what results in improving approximations. We also discuss applications of the proposed techniques in granulating relational databases and concept learning. The last application is particularly interesting, as it shows an approach to concept learning which is more general than approaches based solely on information and decision systems.