{"title":"An Intelligent Approach of Rough Set in Knowledge Discovery Databases","authors":"Hrudaya Ku. Tripathy, B. K. Tripathy, Pradip K. Das","volume":11,"journal":"International Journal of Computer and Information Engineering","pagesStart":3450,"pagesEnd":3454,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/224","abstract":"Knowledge Discovery in Databases (KDD) has\nevolved into an important and active area of research because of\ntheoretical challenges and practical applications associated with the\nproblem of discovering (or extracting) interesting and previously\nunknown knowledge from very large real-world databases. Rough\nSet Theory (RST) is a mathematical formalism for representing\nuncertainty that can be considered an extension of the classical set\ntheory. It has been used in many different research areas, including\nthose related to inductive machine learning and reduction of\nknowledge in knowledge-based systems. One important concept\nrelated to RST is that of a rough relation. In this paper we presented\nthe current status of research on applying rough set theory to KDD,\nwhich will be helpful for handle the characteristics of real-world\ndatabases. The main aim is to show how rough set and rough set\nanalysis can be effectively used to extract knowledge from large\ndatabases.","references":"[1] Ryszard S. Michalski and Kenneth A. Kaufman, \"Data Mining and\nKnowledge Discovery: A Review of Issues and a Multistrategy\nApproach\", Machine Learning and Data Mining, Methods and\nApplications, 1997.\n[2] J.N.Kok and W.A.Kosters, \"Natural Data Mining Techniques\",\nEuropean Association for Theoretical Computer Science, Vol. 71, June\n2000, pp.133-142.\n[3] Ning ZHONG, Andrzej SKOWRON, \"A Rough Set-Based Knowledge\nDiscovery Process\", International Journal of Applied Mathematical\nComputer Science, 2001, Vol.11, No.3, pp.603-619.\n[4] Terje L\u251c\u00a9ken, \"Rough Modeling Extracting Compact Models from Large\nDatabases\", Knowledge Systems Group IDI, A thesis submitted to\nNorwegian University of Science and Technology, 1999.\n[5] U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, \"From data mining to\nknowledge discovery in databases\", Artificial Intelligence Magazine 17\n(1996), pp.37-54.\n[6] Ivo D\u251c\u255dntsch, G\u251c\u255dnther Gediga, Hung Son Nguyen, \"Rough set data\nanalysis in the KDD process\", published in the Proceedings of IPMU\n2000, pp. 220-226.\n[7] Hayri Sever, \"The Status of Research on Rough Sets for Knowledge\nDiscovery in Databases\", www. cuadra.cr.usgs.gov\/pubs\/srj98.pdf\n[8] Deogun.J,Choubey.S,Raghavan.V and Sever.H, \"Feature selection and\neffective classifiers\", Journal of ASIS 49, 5 (1998), pp.423-434.\n[9] Bell.D, and Guan.J, \"Computational methods for rough classification\nand discovery\", Journal of ASIS 49, 5 (1998), pp.403-414.\n[10] Deogun.J.S, Raghavan.V.V, and Sever.H, \"Exploiting upper\napproximations in the rough set methodology\", In The First\nInternational Conference on Knowledge Discovery and Data Mining\n(Montreal, Quebec, Canada, aug 1995), U. Fayyad and R. Uthurusamy,\nEds., pp.69-74.\n[11] Kent.R. E, \"Rough concept analysis\", In Proceedings of the\nInternational Workshop on Rough Sets and Knowledge Discovery\n(Banff, Alberta, Canada, 1993), pp.245-253.\n[12] Andrzej Skowron, \"Rough Sets in KDD\", Institute of Mathematics\nWarsaw University Banacha 2, 02{095, Warsaw, Poland.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 11, 2007"}