Rule discovery based on rough set theory

Abstract

The volume of data being generated nowadays is increasingly large. How to extract useful information from such data collections is an important issue. A promising technique is rough set theory, a new mathematical approach to data analysis based on the classification of objects of interest into similarity classes which are indiscernible with respect to some features. This theory offers two fundamental concepts: reduct and core. In this paper, some basic ideas of rough set theory are first presented, followed by a new heuristic approach for rule induction that is outlined using an illustrative example. Some experimental results are also given.