Abstract
In this paper, we introduce a new type of reducts called the λ-Fuzzy-Reduct, where the fuzzy similarity relation is constructed by means of cosine-distances of decision vectors and the parameter λ is used to tune the similarity precision level. The λ-Fuzzy-Reduct can eliminate harsh requirements of the distribution reduct, and it is more flexible than the maximum distribution reduct, the traditional reduct, and the generalized decision reduct. Furthermore, we prove that the distribution reduct, the maximum distribution reduct, and the generalized decision reduct can be converted into the traditional reduct. Thus in practice the implementations of knowledge reductions for the three types of reducts can be unified into efficient heuristic algorithms for the traditional reduct. We illustrate concepts and methods proposed in this paper by an example.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pawlak, Z.: Some issues on Rough Sets. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 1–58. Springer, Heidelberg (2004)
Zhang, W., Mi, J., Wu, W.: Approaches to knowledge reductions in inconsistent information systems. International journal of intelligent systems 18, 989–1000 (2003)
Wen-Xiu, Z., Ju-Sheng, M., Wei-Zhi, W.: Konwledge Reductions in Inconsistent Information Systems. Chinese Journal of Computer 26(1), 12–18 (2003)
Ziarko, W.: Variable precision rough set model. Journal of Computer Systems and Science 46, 39–59 (1993)
Nguyen, H.S., Slezak, D.: Approximation reducts and association rules correspondence and complexity results. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 137–145. Springer, Heidelberg (1999)
Slezak, D.: Searching for dynamic reducts in inconsistent decision tables. In: Proceedings of IPMU 1998, Paris, pp. 1362–1369 (1998)
Kryszkiewicz, M.: Comparative study of alternative type of knowledge reduction in inconsistent systems. International journal of intelligent systems 16, 105–120 (2001)
Nanda, S.: Fuzzy rough sets. Fuzzy Sets and Systems 45, 157–160 (1992)
Banerjee, M., Pal, S.K.: Roughness of a fuzzy set. Information Science 93, 235–246 (1996)
Lam, W., Ruiz, M., Srinivasan, P.: Automatic taxt categegorization and its application to text retrieval. IEEE Transaction on Knowldge and Data Engineering 11(6), 865–879 (1999)
Dubois, Y., Prade, H.: Fuzzy Sets and Systems-Theory and Applications. Academic Press, New York (1980)
Qihe, L., Fan, L., Fan, M.: An efficient knowledge reduction algorithm based on new conditional information entropy. Control and Decision 20(8), 878–882 (2005)
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
Liu, Q., Chen, L., Zhang, J., Min, F. (2006). Knowledge Reduction in Inconsistent Decision Tables. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_69
Download citation
DOI: https://doi.org/10.1007/11811305_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)