Abstract
In this paper, the combination of formal concept analysis and rough set theory is considered. The notion of information concept lattice is presented and some properties are given. We present the reduction theory of information concept lattice and obtain the reduction method. Information concept lattice is compared with rough set theory and concept lattice.
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References
Pawlak, Z.: Rough Set. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)
Greco, S., Matarazzo, B., Slowinski, R.: Rough Sets Methodology for Sorting Problems in Presence of Multiple Attributes and Criteria. European Journal of Operational Research 138, 247–259 (2002)
Kryszkiewicz, M.: Rules in incomplete information systems. Information Sciences 113, 271–292 (1999)
Zhang, W.X., Leung, Y., Wu, W.Z.: Information Systems and Knowledge Discovery. Science Press, Beijing (2003)
Wille, R.: Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel, Dordrecht (1982)
Ganter, B., Wille, R.: Formal Concept Analysis. Mathematical Foundations. Springer, New York (1999)
Zhang, W.X., Wei, L., Qi, J.J.: Attribute Reduction in Concept Lattice Based on Discernibility Matrix. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 157–165. Springer, Heidelberg (2005)
Oosthuizen, G.D.: The Application of Concept Lattice to Machine Learning. Technical Report, University of Pretoria, South Africa (1996)
Kent, R.E.: Rough Concept Analysis: A Synthesis of Rough Sets and Formal Concept Analysis. Fundamenta Informaticae 27, 169–181 (1996)
Deogun, J.S., Saquer, J.: Formal Rough Concept Analysis. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 91–99. Springer, Heidelberg (1999)
Yao, Y.: A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis. In: Tsumoto, S., et al. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 59–68. Springer, Heidelberg (2004)
Yao, Y.Y.: Concept Lattices in Rough Set Theory. In: Proceedings of Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS2004), pp. 796–801 (2004)
Yao, Y.Y., Chen, Y.H.: Rough Set Approximations in Formal Concept Analysis. In: Proceedings of Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS2004), pp. 73–78 (2004)
Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information Systems. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)
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Xu, W., Xu, P., Zhang, Wx. (2007). Information Concept Lattice and Its Reductions. In: Yao, J., Lingras, P., Wu, WZ., Szczuka, M., Cercone, N.J., Ślȩzak, D. (eds) Rough Sets and Knowledge Technology. RSKT 2007. Lecture Notes in Computer Science(), vol 4481. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72458-2_7
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DOI: https://doi.org/10.1007/978-3-540-72458-2_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72457-5
Online ISBN: 978-3-540-72458-2
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