Knowledge Discovery by Relation Approximation: A Rough Set Approach

In recent years, rough set theory [1] has attracted attention of many researchers and practitioners all over the world, who have contributed essentially to its de-velopment and applications. With many practical and interesting applications rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, de-cision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition [2]. The common issue of the above mentioned domains is the concept approx-imation problem which is based on searching for description – in a predefined language L – of concepts definable in other language L∗. Not every concept in L ∗ can be exactly described in L, therefore the problem is to find an approx-imate description rather than exact description of unknown concepts, and the approximation is required to be as exact as possible. Usually, concepts are inter-pretable as subsets of objects from a universe, and the accuracy of approximatio