TY - JFULL AU - Hrudaya Ku. Tripathy and B. K. Tripathy and Pradip K. Das PY - 2007/12/ TI - An Intelligent Approach of Rough Set in Knowledge Discovery Databases T2 - International Journal of Computer and Information Engineering SP - 3449 EP - 3453 VL - 1 SN - 1307-6892 UR - https://publications.waset.org/pdf/224 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 11, 2007 N2 - Knowledge Discovery in Databases (KDD) has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) interesting and previously unknown knowledge from very large real-world databases. Rough Set Theory (RST) is a mathematical formalism for representing uncertainty that can be considered an extension of the classical set theory. It has been used in many different research areas, including those related to inductive machine learning and reduction of knowledge in knowledge-based systems. One important concept related to RST is that of a rough relation. In this paper we presented the current status of research on applying rough set theory to KDD, which will be helpful for handle the characteristics of real-world databases. The main aim is to show how rough set and rough set analysis can be effectively used to extract knowledge from large databases. ER -