Computational methods for rough classification and discovery

Rough set theory is a new mathematical tool to deal with vagueness and uncertainty. To apply the theory, it is important to associate it with efficient and effective computational methods. With a little adjustment, a relation can be used to represent a decision table for use in decision making. By using this kind of table, rough set theory can be applied successfully to rough classification and knowledge discovery. We present computational methods for using rough sets to identify classes in datasets, finding dependencies in relations, and discovering rules which are hidden in databases. The methods are illustrated with a running example from a database of car test results.