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A rough set approach to knowledge discovery

2002; Wiley; Volume: 17; Issue: 2 Linguagem: Inglês

10.1002/int.10010

1098-111X

James F. Peters, Andrzej Skowron,

Rough Sets and Fuzzy Logic

International Journal of Intelligent SystemsVolume 17, Issue 2 p. 109-112 Full Access A rough set approach to knowledge discovery J. F. Peters, Corresponding Author J. F. Peters [email protected] Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, CanadaElectrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, CanadaSearch for more papers by this authorA. Skowron, A. Skowron Institute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, PolandSearch for more papers by this author J. F. Peters, Corresponding Author J. F. Peters [email protected] Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, CanadaElectrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 2N2, CanadaSearch for more papers by this authorA. Skowron, A. Skowron Institute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, PolandSearch for more papers by this author First published: 29 January 2002 https://doi.org/10.1002/int.10010Citations: 27AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat References 1 Pawlak Z. Rough sets. Int J of Computer and Information Sciences 1982; 11: 341–356. 2 Pawlak Z. Rough sets: Theoretical aspects of reasoning about data. Boston, MA: Kluwer Academic Publishers. 3 Komorowski J, Pawlak Z, Polkowski L, Skowron A. Rough sets: A tutorial. 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Boca Raton, FL: CRC Press; 1996. pp 1494–1500. 10 Grzymala-Busse JW, Grzymala-Busse WJ, Goodwin LK. A comparison of three closest fit approaches to missing attribute values in preterm birth data. International Journal of Intelligent Systems 2001; 17(2). 11 Kryszkiewicz M. Comparative study of alternative types of knowledge reduction in inconsistent systems. International Journal of Intelligent Systems 2000; 16(1): 105–120. 12 Grzymala-Busse JW. LERS—A knowledge discovery system. In: L Polkowski, editors. Rough sets in knowledge discovery, Vol 2. Berlin: Springer-Verlag; 1998. pp 562–565. 13 Grzymala-Busse JW, Stefanowski J. Three discretization methods for rule induction. International Journal of Intelligent Systems 2000; 16(1): 29–38. 14 Kryszkiewicz M. Rough set approach to incomplete information systems. In: PP Wang, editor. Proc of the Int Workshop on Rough Sets Soft Computing at the Second Annual Joint Conf on Information Sciences (JCIS'95). Wrightsville Beach, North Carolina; 28 September–1 October, 1995. pp 194–197. 15 Kryszkiewicz M. Generation of rules from incomplete information systems. In: J Komorowski, editors. The First European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'97), Trondheim, Norway, 25–27 June, 1997. Lecture in Artificial Intelligence 1263. Berlin: Springer-Verlag; 1997. pp 156–166. 16 Peters JF, Skowron A, guest editors. A rough set approach to reasoning about data. International Journal of Intelligent Systems 2001; 16(1): 1–148. 17 Maitan J, Ras Z, Zemankova M. Query handling and learning in a distributed intell system. In: Z Ras, editor. Methodologies for intelligent systems IV. North Holland; 1989. pp 118–127. 18 Ras Z, Zemankova M. Intelligent query processing in distributed information systems. In: ZW Ras, editors. Intelligent systems: State of the art and future directions. London, England: Ellis Horwood Series in Artificial Intelligence; 1990. pp 335–370. 19 Grzymala-Busse JW, Goodwin LK. Predicting preterm birth risk using machine learning from data with missing values. Bull of the International Rough Set Society 1997; 1: 17–21. 20 Alpigini JI. A paradigm for the visualization of dynamic system performance using methodologies derived from chaos theory. PhD thesis, University of Wales, Swansea, UK; 1999. Citing Literature Volume17, Issue2Special Issue: A Rough Sets Approach to Knowledge DiscoveryFebruary 2002Pages 109-112 ReferencesRelatedInformation