Fuzzy Rough Granular Self Organizing Map

  • Avatharam Ganivada
  • Shubhra Sankar Ray
  • Sankar Kumar Pal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6954)


A fuzzy rough granular self organizing map (FRGSOM) is proposed for clustering patterns from overlapping regions using competitive learning of the Kohonen’s self organizing map. The development strategy of the FRGSOM is mainly based on granular input vector and initial connection weights. The input vector is described in terms of fuzzy granules low, medium or high, and the number of granulation structures depends on the number of classes present in the data. Each structure is developed by a user defined α-value, labeled according to class information, and presented to a decision system. This decision system is used to extract domain knowledge in the form of dependency factors using fuzzy rough sets. These factors are assigned as the initial connection weights of the proposed FRGSOM, and then the network is trained through competitive learning. The effectiveness of the FRGSOM is shown on different real life data sets.


fuzzy rough sets rule based layered network fuzzy reflexive relation unsupervised learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pal, S.K.: Computational theory perception (CTP), rough-fuzzy uncertainty analysis and mining in bioinformatics and web intelligence: A unified framework. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets XI. LNCS, vol. 5946, pp. 106–129. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-neural Computing: Techniques for computing with words, pp. 543–553. Springer-Verlag, Germany (2004)CrossRefGoogle Scholar
  3. 3.
    Pal, S.K., Dasgupta, B., Mitra, P.: Rough self organizing map. Applied Intelligence 21, 289–299 (2004)CrossRefMATHGoogle Scholar
  4. 4.
    Mitra, S., Pal, S.K.: Self-organizing neural network as a fuzzy classifier. IEEE Transactions on Systems, Man and Cybernetics 24, 385–399 (1994)CrossRefGoogle Scholar
  5. 5.
    Pawlak, Z.: Rough sets: Theoretical aspects of reasoning about data. Kluwer, Netherlands (1991)CrossRefMATHGoogle Scholar
  6. 6.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. International Journal of General Systems 17, 91–209 (1990)CrossRefMATHGoogle Scholar
  7. 7.
    Banerjee, M., Pal, S.K.: Roughness of a fuzzy set. Information Sciences 93, 235–246 (1996)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Kohonen, T.: Self organizing maps. Proc. IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  9. 9.
    Zadeh, L.A.: From computing with numbers to computing with words-From manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems 46, 105–119 (1999)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets, and classification. IEEE Transactions on Neural Networks 3, 683–697 (1992)CrossRefGoogle Scholar
  11. 11.
    Cornelis, C., Jensen, R., Hurtado, G., Slezak, D.: Attribute selection with fuzzy decision reducts. Information Sciences 180, 209–224 (2010)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Pal, S.K., Dutta Majumder, D.: Fuzzy sets and decision making approaches in vowel and speaker recognition. IEEE Transactions on Systems, Man, and Cybernetics 7, 625–629 (1977)CrossRefMATHGoogle Scholar
  13. 13.
    Hayashi, Y.: Neural expert system using fuzzy teaching input and its application to medical diagnosis. Information Sciences Applications 1, 47–58 (1994)CrossRefMATHGoogle Scholar
  14. 14.
    Ganivada, A., Dutta, S., Pal, S.K.: Fuzzy rough granular neural networks, fuzzy granules, and classification. Theoretical Computer Science (2011), doi:10.1016/j.tcs.2011.05.038.Google Scholar
  15. 15.
    Herbert, J.P., Yao, J.: A granular computing framework for delf-organizing maps. Neurcomputing 72, 2865–2872 (2009)CrossRefGoogle Scholar
  16. 16.
    Yao, Y.Y.: Combination of rough and fuzzy sets based on alpha-level sets. In: Lin, T.Y., Cercone, N. (eds.) Rough sets and Data Mining: Analysis for Imprecise Data, pp. 301–321. Kluwer, Boston (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Avatharam Ganivada
    • 1
  • Shubhra Sankar Ray
    • 1
  • Sankar Kumar Pal
    • 1
  1. 1.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

Personalised recommendations