Divisible Rough Sets Based on Self-organizing Maps

  • Rocío Martínez-López
  • Miguel A. Sanz-Bobi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3776)


The rough sets theory has proved to be useful in knowledge discovery from databases, decision-making contexts and pattern recognition. However this technique has some difficulties with complex data due to its lack of flexibility and excessive dependency on the initial discretization of the continuous attributes. This paper presents the divisible rough sets as a new hybrid technique of automatic learning able to overcome the problems mentioned using a combination of variable precision rough sets with self-organizing maps and perceptrons. This new technique divides some of the equivalence classes generated by the rough sets method in order to obtain new certain rules under the data which originally were lost. The results obtained demonstrate that this new algorithm obtains a higher decision-making success rate in addition to a higher number of classified examples in the tested data sets.


  1. 1.
    Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Pawlak, Z., Grzymala-Busse, J., Slowinski, R., Ziarko, W.: Rough Sets. Communications of the ACM 38(11), 89–95 (1995)CrossRefGoogle Scholar
  3. 3.
    Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Sciences 46(1), 39–59 (2003)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Kohonen, T.: Self-organizing Formation of Topologically Correct Feature Maps. Biological Cybernetics 43(1), 59–69 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Shen, L., Tay, F.E.H.: A Discretization Method for Rough Sets Theory. Intelligent Data Analysis 5, 431–438 (2001)zbMATHGoogle Scholar
  6. 6.
    Browne, C., Düntsch, I., Gediga, G.: IRIS Revisited, A Comparison of Discriminant and Enhanced Rough Set Data Analysis. In: Polkowski, L., Slowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 2, pp. 345–368. Physica Verlag, Heidelberg (1998)Google Scholar
  7. 7.
    Banarjee, M., Mitra, S., Pal, S.K.: Rough Fuzzy MLP: Knowledge Encoding and Classification. IEEE Transactions on Neural Networks 9, 1203–1216 (1998)CrossRefGoogle Scholar
  8. 8.
    Nguyen, H.S., Szczuka, M.S., Slezak, D.: Neural Networks Design: Rough Set Approach to Continuous Data. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 359–366. Springer, Heidelberg (1997)Google Scholar
  9. 9.
    Pal, S.K., Dasgupta, B., Mitra, P.: Rough Self Organizing Map. Applied Intelligence 21(3), 289–299 (2004)zbMATHCrossRefGoogle Scholar
  10. 10.
    Ziarko, W.: Acquisition of Hierarchy-Structured Probabilistic Decision Tables and Rules from Data. Expert Systems 20(5), 305–310 (2003)CrossRefGoogle Scholar
  11. 11.
    Merz, C.J., Murphy, P.M.: UCI repository of machine learning databases. Irvine, CA: University of California, Department of Information and Computer Science (1996),
  12. 12.
    Nguyen, H.S., Nguyen, S.H.: Discretization Methods in Data Mining. In: Polkowski, L., Slowron, A. (eds.) Rough Sets in Knowledge Discovery, vol. 1, pp. 451–482. Physica Verlag, Heidelberg (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Rocío Martínez-López
    • 1
  • Miguel A. Sanz-Bobi
    • 1
  1. 1.Computer Science Department, Escuela Técnica Superior de Ingeniería-ICAIUniversidad Pontificia ComillasMadridSpain

Personalised recommendations