Abstract
An important issue of knowledge discovery and data mining is the reduction of pattern dimensionality. In this paper, we investigate the attribute reduction in decision systems based on a congruence on the power set of attributes and present a method of determining congruence classifications. We can obtain the reducts of attributes in decision systems by using the classification. Moreover, we prove that the reducts obtained by the congruence classification coincide with the distribution reducts in decision systems.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pawlak, Z.: Rough sets. International J. Comp. Inform. Science 11, 341–356 (1982)
Pawlak, Z.: Rough Sets: Theoretical Aspects to Reasoning about Data. Kluwer Academic Publisher, Boston (1991)
Jensen, R., Shen, Q.: Fuzzy-rough attribute reduction with application to web categorization. Fuzzy Sets and Systems 141, 469–485 (2004)
Li, H.R., Zhang, W.X.: Applying Indiscernibility Attribute Sets to Knowledge Reduction. In: Zhang, S., Jarvis, R. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 816–821. Springer, Heidelberg (2005)
Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159, 255–272 (2004)
Zhang, W.X., Leung, Y., Wu, W.Z.: Information Systems and Knowledge Discovery. Science Press, Beijing (2003)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski (ed.) Intelligent Decision Support-Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic, Dordrecht (1992)
Ziarko, W.: Analysis of uncertain information in the framework of variable precision rough sets. Foundations of Computing and Decision Sciences 18, 381–396 (1993)
Slowinski, R., Stefanowski, J., Greco, S., et al.: Rough set based processing of inconsistent information in decision analysis. Control Cybernet. 1, 379–404 (2000)
Novotný, M.: Dependence Spaces of Information Systems. In: Orlowska, E. (ed.) Incomplete Informations: Rough Sets Analysis, pp. 193–246. Physica-Verlag (1998)
Lin, T.Y., Liu, Q.: Rough approximate operators: axiomatic rough set theory. In: Ziarko, W. (ed.) Rough Sets, Fuzzy Sets and Knowledge Discovery, pp. 256–260. Springer, Berlin (1994)
Wang, G.Y.: Uncertainty Measurement of Decision Table Information Systems. Computer Sciences 28(5, Special Issues), 23–26 (2001)
Wu, W.Z., Mi, J.S., Zhang, W.X.: Generalized fuzzy rough sets. Information Sciences 151, 263–282 (2003)
Yao, Y.Y.: A comparative study of fuzzy sets and rough sets. Journal of Information Sciences 109, 227–242 (1998)
Yager, R.R.: Modeling uncertainty using partial information. Information Sciences 121, 271–294 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, H., Zhang, W., Xu, P., Wang, H. (2006). Rough Set Attribute Reduction in Decision Systems. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_20
Download citation
DOI: https://doi.org/10.1007/11795131_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)