Application of Rough Sets in Pattern Recognition

  • Sushmita Mitra
  • Haider Banka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4400)


This article provides an overview of recent literature on some tasks of pattern recognition using rough sets and its hybridization with other soft computing paradigms. Rough set theory is an established tool for dealing with imprecision, noise, and uncertainty in data. In this article we will focus on two recent applications using rough sets; viz., feature selection in high dimensional gene expression data, and collaborative clustering. The experimental results demonstrate that the incorporation of rough set improves the performance of the system.


Feature Selection Discernibility Matrix Cluster Prototype Indiscernibility Relation Redundancy Reduction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pawlak, Z.: Rough Sets, Theoretical Aspects of Reasoning about Data. Kluwer Academic, Dordrecht (1991)zbMATHGoogle Scholar
  2. 2.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic, Dordrecht (1992)Google Scholar
  3. 3.
    Wroblewski, J.: Finding minimal reducts using genetic algorithms. Technical Report 16/95, Warsaw Institute of Technology - Institute of Computer Science, Poland (1995)Google Scholar
  4. 4.
    Bjorvand, A.T.: ‘Rough Enough’ – A system supporting the rough sets approach. In: Proceedings of the Sixth Scandinavian Conference on Artificial Intelligence, Helsinki, Finland, pp. 290–291 (1997)Google Scholar
  5. 5.
    Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley, London (2001)zbMATHGoogle Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Communications of the ACM 37, 77–84 (1994)CrossRefGoogle Scholar
  7. 7.
    Mitra, S., Acharya, T.: Data Mining: Multimedia, Soft Computing, and Bioinformatics. John Wiley, New York (2003)Google Scholar
  8. 8.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)zbMATHGoogle Scholar
  9. 9.
    Lingras, P., West, C.: Interval set clustering of Web users with rough k-means. Technical Report No. 2002-002, Dept. of Mathematics and Computer Science, St. Mary’s University, Halifax, Canada (2002)Google Scholar
  10. 10.
    Pedrycz, W.: Collaborative fuzzy clustering. Pattern Recognition Letters 23, 1675–1686 (2002)zbMATHCrossRefGoogle Scholar
  11. 11.
    Deb, K., et al.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2002)CrossRefGoogle Scholar
  12. 12.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research 5, 1205–1224 (2004)MathSciNetGoogle Scholar
  13. 13.
    Banerjee, M., Mitra, S., Banka, H.: Evolutionary-rough feature selection in gene expression data. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews (to appear)Google Scholar
  14. 14.
    Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, London (1974)zbMATHGoogle Scholar
  15. 15.
    Mitra, S., Banka, H., Pedrycz, W.: Rough-fuzzy collaborative clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 36, 795–805 (2006)CrossRefGoogle Scholar
  16. 16.
    Mitra, S.: An evolutionary rough partitive clustering. Pattern Recognition Letters 25, 1439–1449 (2004)CrossRefGoogle Scholar
  17. 17.
    Special Issue on Bioinformatics. IEEE Computer 35 (2002)Google Scholar
  18. 18.
    Ferber, J.: Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence. Addison-Wesley Longman, New York (1999)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Sushmita Mitra
    • 1
  • Haider Banka
    • 1
  1. 1.Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata 700 108India

Personalised recommendations