Rough Knowledge Discovery and Applications

Abstract

This work has two objectives: first, to introduce rough set theory, developed by Pawlak, to a wider audience; second, to present computational methods for the theory, allowing it to be implemented in many more systems. Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence and cognitive sciences. Although the burgeoning methodology has been successful in many real- life applications, there are still several theoretical problems to be solved. We need a practical approach to apply the theory. Some problems, for example, the general problem of finding all reducts, are NP-hard. Thus, it is important to investigate computational methods for the theory. We present computational methods for the theory of rough sets and knowledge discovery in databases. Emphasizing applications, we illustrate our methods by means of running examples using data of flu diagnosis.