Generalizations of rough set concepts

Journal of King Saud University - Science

Volumn 23, Issue 1, 2011, Pages 17-21

  Salama, A S ^a,b   Abd El Monsef, M M E ^a  

^a Tanta University   (Egypt)

^b SHAQRA UNIVERSITY   (Saudi Arabia)

Author keywords

Rough approximations; Rough measures; Rough sets; Topological spaces

Indexed keywords

EID: 79951850404     PISSN: 10183647     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jksus.2010.06.001     Document Type: Article

References (34)

1. Duntsch, I., Gediga, G., Orlowska, E., 2001. Relational attribute systems. International Journal of Human-Computer Studies 55, 293-309.
2. Greco, S., Matarazzo, B., Slowinski, R., 2001. Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129, 1-47.
3. Greco, S., Matarazzo, B., Slowinski, R., 2002. Rough approximation by dominance relation. International Journal of Intelligent Systems 17, 153-171.
4. Hu, X.-H., Cercone, N., 1995. Learning in relational databases: a rough set approach. Computational Intelligence 11 (2), 323-337.
5. Kryszkiewicz, M., 1998. Rough set approach to incomplete information systems. Information Sciences 112, 39-49.
6. Kryszkiewicz, M., 1999. Rules in incomplete information systems. Information Sciences 113, 271-292.
7. Lipski, W., 1981. On databases with incomplete information. Journal of the ACM 26, 41-70.
8. Lin, T.Y., 1988. Neighborhood systems and relational database. In: Proceedings of CSC'88.
9. Lin, T.Y., Yao, Y.Y., 1996. Mining soft rules using rough sets and neighborhoods. In: Proceedings of the Symposium on Modelling, Analysis and Simulation, Computational Engineering in Systems Applications (CESA'96), IMASCS Multiconference, Lille, France, July 9-12, pp. 1095-1100.
10. Lingras, P.J., Yao, Y.Y., 1998. Data mining using extension of rough set model. Journal of American Society of Information Science 49 (5), 415-422.
11. Leung, Y., Li, D.-Y., 2003. Maximal consistent block technique for rule acquisition in incomplete information systems. Information Sciences 153, 85-106.
12. Orlowska, Ewa, 1986. Semantics analysis of inductive reasoning. Theoretical Computer Science 43, 81-89.
13. Orlowska, E., Pawlak, Z., 1987. Representation of nondeterministic information. Theoretical Computer Science 29, 27-39.
14. Pawlak, Z., 1982. Rough sets. International Journal of Computer and Information Sciences 11, 341-356.
15. Pawlak, Z., 1991. Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, London.
16. Pawlak, Z., Slowinski, R., 1994. Rough set approach to multi-attribute decision analysis, Invited Review. European Journal of Operational Research 72, 443-459.
17. Pawlak, Z., 1997. Rough set approach to knowledge-based decision support. European Journal of Operational Research 99, 48-57.
18. Pawlak, Z., 1998. Rough set theory and its applications in data analysis. International Journal of Cybernetics Systems 29, 661-685.
19. Pawlak, Z., 2001. Drawing conclusions from data-the rough set way. International Journal of Intelligent Systems 16, 3-11.
20. Skowron, A., Rauszer, C., 1992. The discernibility matrices and functions in information systems. In: Slowinski, R. (Ed.), Intelligent Decision Support: Handbook of Applications and Advances of Rough Sets Theory. Kluwer Academic Publisher, Dordrecht, pp. 331-362.
21. Skowron, A., 1993. Boolean reasoning for decision rules generation. In: Komorowski, J., Ras, Z. (Eds.), Proceedings of the 7th International Symposium ISMIS'93, Trondheim, Norway, 1993, Lecture Notes in Artificial Intelligence, vol. 689. Springer, Berlin, pp. 295-305.
22. Skowron, A., 1995. Extracting laws from decision tables: a rough set. Computational Intelligence 110, 371-388.
23. Skowron, A., Stepaniuk, J., 1995. Generalized approximation space. In: Lin, T.Y., Wildberger, A.M. (Eds.), Soft Computing, Simulation Councils, San Diego, pp. 18-21.
24. Skowron, A., Stepaniuk, J., 1996. Tolerance approximation spaces. Fundamenta Informaticae 27 (2-3), 245-253.
25. Slowinski, R., Stepaniuk, J., 1989. Rough classification in incomplete information systems. Mathematical and Computer Modelling 12 (10/11), 1347-1357.
26. Slowinski, R., Vanderpooten, D., 1997. Similarity relation as a basis for rough approximations. In: Wang, P.P. (Ed.), Advances in Machine Intelligence and Soft-Computing. Department of Electrical Engineering, Duke University Durham, NC, USA, pp. 17-33.
27. Slowinski, R., Vanderpooten, D., 2000. A generalized definition of rough approximations based on similarity. IEEE Transactions on Knowledge and Data Engineering 12 (2), 331-336.
28. Stepaniuk, J., 1998. Approximation spaces, reducts and representatives. In: Polkowski, L., Skowron, A. (Eds.), Rough Sets in Knowledge Discovery 2. Applications, Case Studies and Software Systems. Physica-Verlag, Heidelberg, pp. 109-126.
29. Stepaniuk, J., 2000. Knowledge discovery by application of rough set methods. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (Eds.), Rough Set Methods and Applications. Physica-Verlag, Heidelberg, pp. 213-234.
30. Yao, Y.Y., Wong, S.K.M., 1995. Generalization of rough sets using relationships between attribute values. In: Proceedings of the 2nd Annual Joint Conference on Information Sciences, pp. 30-33.
31. Yao, Y.Y., Lin, T.Y., 1996. Generalization of rough sets using modal logic. Intelligent Automation and Soft Computing 2, 103-120.
32. Yao, Y.Y., 1996. Two views of the theory of rough sets in finite universes. International Journal of Approximate Reasoning 15, 291-317.
33. Yao, Y.Y., 1998. Relational interpretation of neighborhood operators and rough set approximation operator. Information Sciences 111, 239-259.
34. Zhang, W.-X., Mi, J.-S., Wu, W.-Z., 2003. Approaches to knowledge reducts in inconsistent systems. International Journal of Intelligent Systems 18, 989-1000.