Rough sets

Encyclopedia of Database Technologies and Applications

Volumn , Issue , 2005, Pages 575-580

  Pawlak, Zdzislaw ^a   Polkowski, Lech ^b   Skowron, Andrzej ^c  

^a WARSAW SCHOOL OF INFORMATION TECHNOLOGY   (Poland)

^b POLISH JAPANESE INSTITUTE OF INFORMATION TECHNOLOGY   (Poland)

^c UNIVERSITY OF WARSAW   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 79951500895     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59140-560-3.ch095     Document Type: Chapter

References (19)

1. Bazan, J., Nguyen, H. S., Nguyen, S. H., Synak, P., & Wróblewski, J. (2000). Rough set algorithms in classification problems. In L. Polkowski, S. Tsumoto, & T. Y. Lin (Eds.), Rough set methods and applications: New developments in knowledge discovery in information systems (pp. 49-88). Heidelberg, Germany: Physica-Verlag.
2. Demri, S., & Orlowska, E. (2002). Incomplete information: Structure, inference, complexity. Heidelberg, Germany: Springer-Verlag.
3. Kloesgen, W., & Zytkow, J. (Eds.). (2002). Handbook of knowledge discovery and data mining. Oxford, UK: Oxford University Press.
4. Lin, T. Y., & Cercone, N. (1997). Rough sets and data mining: Analysis of imperfect data. Boston: Kluwer Academic.
5. Lukasiewicz, J. (1970). Die logischen Grundlagen der Wahrscheinlichkeitsrechnung (1913). In L. Borkowski (Ed.), Jan Lukasiewicz: Selected works, Amsterdam; Warsaw, Poland: Polish Scientific Publishers and NorthHolland
6. Pal, S. K., Polkowski, L., & Skowron, A. (Eds.). (2004). Rough-neural computing: Techniques for computing with words. Berlin, Germany: Springer-Verlag.
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9. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht, Germany: Kluwer.
10. Pawlak, Z., & Skowron, A. (1994). Rough membership functions.InR. R. Yager, M. Fedrizzi, & J. Kacprzyk (Eds.), Advances in the Dempster-Schafer theory of evidence (pp. 251-271). New York: Wiley.
11. Polkowski, L. (2002). Rough sets: Mathematical foundations. Heidelberg, Germany: Physica-Verlag.
12. Polkowski, L., Tsumoto, S., & Lin, T. Y. (Eds.). (2000). Rough set methods and applications: New developments in knowledge discovery in information systems. Heidelberg, Germany: Physica-Verlag.
13. Polkowski, L., & Skowron, A. (1996). Rough mereology: A new paradigm for approximate reasoning., International Journal of Approximate Reasoning, 15(4), 333-365.
14. Polkowski, L., & Skowron, A. (Eds.). (1998). Rough sets in knowledge discovery 1 & 2. Heidelberg, Germany: Physica-Verlag.
15. Russell, S.J., & Norvig, P. (2003). Artificial intelligence. A modern approach. Saddle River, NJ: Prentice Hall.
16. Skowron, A., & Rauszer, C. (1992). The discernibility matrices and functions in information systems. In R. Slowiñski (Ed.), Intelligent decision support: Handbook of applications and advances of the rough sets theory (pp. 331-362). Dordrecht, Germany: Kluwer.
17. Skowron, A., & Stepaniuk, J. (2001). Information granules: Towards foundations of granular computing. International Journal of Intelligent Systems, 16(1), 57-86.
18. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
19. Zadeh, L. A. (2001). A new direction in AI: Toward a computational theory of perceptions. AI Magazine, 22(1), 73-84.