Abstract
Reduct and core computation is one of the key problems in rough set theory due to its applications in data mining extensively. Much attention presently has paid to it in compatible information system. However, in practice, many information systems are incompatible because of noise or incomplete data. In this paper, reduct and core computation for incompatible information systems is studied based on the algebraic view. A new method to construct discernibility matrix is proposed, which is a generalization of the methods presented by Hu(1995), Ye(2002) and Qing(2003). Moreover, the results are suitable for compatible information systems.
This work was partially supported by the National Natural Science Foundation of China (NSFC) under the grant No.60074014 and the Basic Science Foundation of Southwest Jiaotong University.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer, Dordrecht (1991)
Polkowski, L., Skowron, A. (eds.): Rough Sets in Knowledge Discovery. Physica-Verlag, Heidelberg (1998)
Skowron, A., Rauszer, C.: The Discernibility Matrixes and Functions in Information System. In: Slowinski, R. (ed.) Intelligent Decision Support Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer, Dordrecht (1992)
Wang, J., Wang, J.: Reduction Algorithm Based on Discernibility Matrix: The Ordered Attribute Method. Journal computer science and Technology 16, 489–504 (2001)
Wang, G.Y.: Calculation Methods for Core Attributes of Decision Table. Chinese Journal of Computers 26, 611–615 (2003)
Miao, D.Q., Hu, G.R.: A Heuristic Algorithm for Reduction of Knowledge (in Chinese) Journal of Computer Research and Development 36, 681–684 (1999)
Slezak, D.: Searching for dynamic reducts in inconsistent decision tables. In: Proceedings of IPMU 1998, Paris, France, vol. 2, pp. 1362–1369 (1998)
Li, T.R., Xu, Y.: A Generalized Rough Set Approach to Attribute Generalization in Data Mining. In: FLINS 2000, Bruges, Belgium, pp. 126–133. World Scientific, Singapore (2000)
Chang, L.Y., Wang, G.Y., Wu, Y.: An Approach for Attribute Reduction and Rule Generation Based on Rough Set Theory(in Chinese). Journal of software 10, 1206–1211 (1999)
Liu, Q., Liu, S.H., Zheng, F.: Rough Logic and its Applications in data Reduction (in Chinese). Journal of Software 12, 415–419 (2001)
Kryszkiewicz, M.: Comparative Studies of Alternative Type of Knowledge Reduction in Inconsistent Systems. International Journal of Intelligent Systems 16, 105–120 (2001)
Hu, X., Cercone, N.: Learning in Relational Databases: a Rough Set Approach. J. Computational Intelligence 2, 323–337 (1995)
Ye, D.Y., Chen, Z.J.: A New Discenibility Matrix and the Computation of a Core. Acta Electronica Sinica 30, 1086–1088 (2002)
Zhang, W.X., Mi, J.S., Wu, W.Z.: Approaches to Knowledge Reducts in Inconsistent Systems. Chinese Journal of Computers 26, 12–18 (2003)
Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to Approximation Reducts in Inconsistent Decision Tables. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 283–286. Springer, Heidelberg (2003)
Qing, K.Y., et al.: Reduction of Decision Table and Computation of Core, TR- 03-16, Southwest Jiaotong University, 1-8, submitted to Chinese Journal of Computer (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, Tr., Qing, Ky., Yang, N., Xu, Y. (2004). Study on Reduct and Core Computation in Incompatible Information Systems . In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds) Rough Sets and Current Trends in Computing. RSCTC 2004. Lecture Notes in Computer Science(), vol 3066. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25929-9_56
Download citation
DOI: https://doi.org/10.1007/978-3-540-25929-9_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22117-3
Online ISBN: 978-3-540-25929-9
eBook Packages: Springer Book Archive