Clustering Based on Fuzzy Rule-Based Classifier

  • D. K. Behera
  • P. K. Patra
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


Clustering is the unsupervised classification of patterns which has been addressed in many contexts and by researchers in many disciplines. Fuzzy clustering is recommended than crisp clustering when the boundaries among the clusters are vague and uncertain. Popular clustering algorithms are K-means, K-medoids, Hierarchical Clustering, fuzzy-c-means and their variations. But they are sensitive to number of potential clusters and initial centroids. Fuzzy rule based Classifier is supervised and is not sensitive to number of potential clusters. By taking the advantages of supervised classification, this paper intended to design an unsupervised clustering algorithm using supervised fuzzy rule based classifier. Fuzzy rule with certainty grade plays vital role in optimizing the rule base which is exploited in this paper. The proposed classifier and clustering algorithm have been implemented in Matlab R2010a and tested with various benchmarked multidimensional datasets. Performance of the proposed algorithm is compared with other popular baseline algorithms.


Clustering Classification Fuzzy clustering Fuzzy rule-based classifier 


  1. 1.
    Xu, R., Wunsch, D.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16, 645–678 (2005)CrossRefGoogle Scholar
  2. 2.
    Likas, A., Vlassis, N., Verbeek, J.: The global K-means clustering algorithm. Pattern Recogn. 36, 451–461 (2003)CrossRefGoogle Scholar
  3. 3.
    Park, H.S., Jun, C.H.: A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36, 3336–3341 (2009)CrossRefGoogle Scholar
  4. 4.
    Yager, R., Filev, D.: Generation of fuzzy rules by mountain clustering. J. Intel. Fuzzy Syst. 2, 209–211 (1994)Google Scholar
  5. 5.
    Baraldi, A., Blonda, P.: A survey of fuzzy clustering algorithms for pattern recognition—parts I and II. IEEE Trans. Syst., Man, Cybern. B, Cybern. 29, 778–801 (1999)CrossRefGoogle Scholar
  6. 6.
    Yuan, B., Klir, G.J., Stone, J.F.: Evolutionary fuzzy c-means clustering algorithm. In: Fuzzy-IEEE, pp. 2221–2226 (1995)Google Scholar
  7. 7.
    Pal, N.R., Pal, K., Keller, J.M., Bezdek, J.C.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13, 517–530 (2005)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Kolen, J., Hutcheson, T.: Reducing the time complexity of the fuzzy c-means algorithm. IEEE Trans. Fuzzy Syst. 10, 263–267 (2002)CrossRefGoogle Scholar
  9. 9.
    Zhu, L., Chung, F.L., Wang, S.: Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Trans. Syst. Man, Cybern. 39, 578–591 (2009)CrossRefGoogle Scholar
  10. 10.
    Wikaisuksakul, S.: A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Appl. Soft Comput. 24, 679–691 (2014)CrossRefGoogle Scholar
  11. 11.
    Wang, L., Leckie, C., Ramamohanarao, K., Bezdek, J.: Automatically determining the number of clusters in unlabeled data sets. IEEE Trans. Knowl. Data Eng. 21, 335–350 (2009)CrossRefGoogle Scholar
  12. 12.
    Alcala-Fdez, J., Alcala, R., Herrera, F.: A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE Trans. Fuzzy Syst. 19, 857–872 (2011)CrossRefGoogle Scholar
  13. 13.
    Sugeno, M., Yasukawa, T.: A fuzzy-logic-based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1, 7–31 (1993)CrossRefGoogle Scholar
  14. 14.
    Setnes, M., Babuska, R., Verbruggen, B.: Rule-based modeling: precision and transparency. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 28, 165–169 (1998)CrossRefGoogle Scholar
  15. 15.
    Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)CrossRefMathSciNetMATHGoogle Scholar
  16. 16.
    Klir, G.J., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Applications. In Pattern Recognition. Prentice-Hall, Englewood Cliffs (1995)Google Scholar
  17. 17.
    Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man-Mach. Stud. 7, 1–13 (1975)CrossRefMATHGoogle Scholar
  18. 18.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken (1990)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Trident Academy of TechnologyBhubaneswarIndia
  2. 2.College of Engineering and TechnologyBhubaneswarIndia

Personalised recommendations