Knowledge Discovery by Relation Approximation: A Rough Set Approach

  • Hung Son Nguyen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4062)


In recent years, rough set theory [1] has attracted attention of many researchers and practitioners all over the world, who have contributed essentially to its development and applications. With many practical and interesting applications rough set approach seems to be of fundamental importance to AI and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition [2].


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Pawlak, Z.: Some issues on rough sets. Transaction on Rough Sets 1, 1–58 (2004)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Bazan, J., Nguyen, H.S., Szczuka, M.: A view on rough set concept approximations. Fundamenta Informatica 59(2-3), 107–118 (2004)zbMATHGoogle Scholar
  4. 4.
    Bazan, J.G., Nguyen, S.H., Nguyen, H.S., Skowron, A.: Rough set methods in approximation of hierarchical concepts. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol. 3066, pp. 346–355. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Nguyen, S.H., Bazan, J., Skowron, A., Nguyen, H.S.: Layered learning for concept synthesis. 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. 187–208. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge (2000)Google Scholar
  7. 7.
    Skowron, A., Pawlak, Z., Komorowski, J., Polkowski, L.: A rough set perspective on data and knowledge. In: Kloesgen, W., Żytkow, J. (eds.) Handbook of KDD, pp. 134–149. Oxford University Press, Oxford (2002)Google Scholar
  8. 8.
    Stepaniuk, J.: Optimizations of rough set model. Fundamenta Informaticae 36(2-3), 265–283 (1998)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Skowron, A., Stepaniuk, J.: Tolerance approximation spaces. Fundamenta Informaticae 27(2-3), 245–253 (1996)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Slowinski, R., Vanderpooten, D.: Similarity relation as a basis for rough approximations. In: P., W. (ed.) Advances in Machine Intelligence & Soft-computing, Bookwrights, Raleigh, pp. 17–33 (1997)Google Scholar
  11. 11.
    Greco, S., Matarazzo, B., Słowiński, R.: Dealing with missing data in rough set analysis of multi-attribute and multi-criteria decision problems. In: Zanakis, S., Doukidis, G., Zopounidis, C. (eds.) Decision Making: Recent Developments and Worldwide Applications, pp. 295–316. Kluwer Academic Publishers, Boston (2000)Google Scholar
  12. 12.
    Slowinski, R., Greco, S., Matarazzo, B.: Rough set analysis of preference-ordered data. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds.) RSCTC 2002. LNCS (LNAI), vol. 2475, pp. 44–59. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Slowinski, R., Greco, S.: Inducing Robust Decision Rules from Rough Approximations of a Preference Relation. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 118–132. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Nguyen, S.H.: Regularity analysis and its applications in data mining. In: Polkowski, L., Lin, T.Y., Tsumoto, S. (eds.) Rough Set Methods and Applications: New Developments in Knowledge Discovery in Information Systems. Studies in Fuzziness and Soft Computing, vol. 56, pp. 289–378. Springer, Heidelberg (2000)Google Scholar
  15. 15.
    Wojna, A.: Analogy based reasoning in classifier construction (In: Transactions on Rough Sets IV: Journal Subline), pp. 277–374Google Scholar
  16. 16.
    Nguyen, H.S., Łuksza, M., Mkosa, E., Komorowski, J.: An Approach to Mining Data with Continuous Decision Values. In: Klopotek, M.A., Wierzchon, S.T., Trojanowski, K. (eds.) Proceedings of the International IIS: IIPWM 2005 Conference held in Gdansk, Poland, June 13-16, 2005. Advances in Soft Computing, pp. 653–662. Springer, Heidelberg (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hung Son Nguyen
    • 1
  1. 1.Institute of MathematicsWarsaw UniversityWarsawPoland

Personalised recommendations