Application of Binary System to Information Table Based on Rough Set

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First the traditional objects-attribute information table converted into a objects-attribute-attribute value table by using binary. Then two new concept is defined in this paper: concept vector instead of elementary sets (the equivalence classes of R) and knowledge matrix instead of equivalence relation of R by using binary system. Put forward a new solution to the lower and upper approximations support subset on rough set by using concept vector and knowledge matrix. Examples also are given.

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2894-2897

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August 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] Z. Pawlak. Rough sets[J]. International Journal of Computer and Information Sciences, 1, 1982, pp.341-356.

Google Scholar

[2] W.H. Xu, X.Y. Zhang, J. M Zhong and W.X. Zhang. Attribute reduction in ordered information systems based on evidence theory. Knowledge and Information Systems. 2010, 25: 69-184.

DOI: 10.1007/s10115-009-0248-5

Google Scholar

[3] Shaobo Deng, Min Li. The simple-discernibility matrix in rough sets. Proc. SPIE 8349, Fourth International Conference on Machine Vision (ICMV 2011): Machine Vision, Image Processing, and Pattern Analysis, 834904 (January 11, 2012).

DOI: 10.1117/12.920084

Google Scholar

[4] HaiTao Wu. A New Discernibility Matrix Based on Distribution Reduction. Proceedings of the International Symposium on Intelligent Information Systems and Applications (IISA'09) . 2009, pp.390-393.

Google Scholar

[5] N. Zhong, A. Skowron, and S. Ohsuga, Eds. New Direction in Rough Sets, Data Mining, and Granular-Soft Computing. Springer, (1999).

Google Scholar

[6] L. Polkowski and A. Skowron, Eds., Rough Sets and Current Trendsin Computing. Lecture Notes in Artificial Intelligence 1424, Springer, (1998).

Google Scholar

[7] L. Polkowski, S. Tsumoto, and T. Y. Lin, Eds., Rough Set Methods and Applications – New Developments in Knowledge Discovery in Information Systems. Springer, (2000).

DOI: 10.1007/978-3-7908-1840-6

Google Scholar

[8] Z. Pawlak. Rough Sets - Theoretical Aspects of Reasoning about Data[M]. Boston: KluwerAcademic Publishers, (1991).

Google Scholar

[9] Iwinski T B. Algebraic approach to rough sets[J]. Bulletin of Polish Academy of Science, Mathematics, 1987, 35: 673-683.

Google Scholar

[10] Hu X. Knowledge Discovery in Database: an attribute-oriented rough set Aapproach[D]. University of Regina, Canada, (1995).

Google Scholar

[11] L. Polkowski and A. Skowron, Eds., Rough Sets in Knowledge Discovery. Vol. 1–2, Springer, (1998).

Google Scholar

[12] L. Polkowski: Rough Sets, Mathematical Foundations, Advances in Soft Computing, Physica – Verlag, A Springer-Verlag Company, (2002).

Google Scholar

[13] A. Skowron et al: Rough set perspective on data and konwedge, Handbook of Data Mining and Knoledge Discovery (W. Klösgen, J. Żytkow eds. ), Oxford University Press, 2002, 134-149.

Google Scholar

[14] Felix R , Ushio T. Rough Sets - based Machine Learning Using a Binary Discernibility Matrix[c]. [s. 1. ]: IPMM , 1999. 299-305.

DOI: 10.1109/ipmm.1999.792493

Google Scholar