Fuzzy C-Means and Fuzzy TLBO for Fuzzy Clustering

  • P. Gopala Krishna
  • D. Lalitha Bhaskari
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


The choice of initial center plays a great role in achieving optimal clustering results in all partitional clustering approaches. Fuzzy C-means is a widely used approach but it also gets trapped in local optima values due to sensitiveness to initial cluster centers. To alleviate this issue, a new approach of using an evolutionary technique known as Teaching–Learning-Based Optimization (TLBO) is used hybridized with fuzzy approach. The proposed approach is able to deal with the sensitiveness of cluster centers. Results presented are very encouraging.


FCM Fuzzy clustering Real-life data sets 


  1. 1.
    Hathaway, R.J., Bezdek, J.C.: Local convergence of the fuzzy c-means algorithms. Pattern Recogn. 19(6), 477–480 (1986)MATHMathSciNetCrossRefGoogle Scholar
  2. 2.
    Selim, S.Z., Alsultan, K.: A simulated annealing algorithm for the clustering problem. Pattern Recogn. 24(10), 1003–1008 (1991)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Al-Sultan, K.S.: A tabu search approach to the clustering problem. Pattern Recogn. 28(9), 1443–1451 (1995)CrossRefGoogle Scholar
  4. 4.
    Hall, L.O., Ozyurt, I.B., Bezdek, J.C.: Clustering with a genetically optimized approach. IEEE Trans. Evol. Comput. 3(2), 103–112 (1999)CrossRefGoogle Scholar
  5. 5.
    Bandyopadhyay, S., Maulik, U.: An evolutionary technique based on k-means algorithm for optimal clustering in rn. Inf. Sci. 146(1–4), 221–237 (2002)MATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Lili, L., Xiyu, L., Mingming, X.: A novel fuzzy clustering based on particle swarm optimization. In: First IEEE International Symposium on Information Technologies and Applications in Education, ISITAE, pp. 88–90 (2007)Google Scholar
  7. 7.
    Kanade, P.M., Hall, L.O.: Fuzzy ants and clustering. IEEE Trans. Syst. Manage. Cybern. Part A 37(5), 758–769 (2007)CrossRefGoogle Scholar
  8. 8.
    Maulik, U., Saha, I.: Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery. Pattern Recogn. 42(9), 2135–2149 (2009)MATHCrossRefGoogle Scholar
  9. 9.
    Paterlini, S., Krink, T.: Differential evolution and particle swarm optimisation in partitional clustering. Comput. Stat. Data Anal. 50(5), 1220–1247 (2006)MATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Rao, R.V., Savsani, V.J., Vakharia, D.P.: Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput. Aided Des. 43, 303–315 (2011)CrossRefGoogle Scholar
  11. 11.
    Rao, R.V., Savsani, V.J.: Mechanical design optimization using advanced optimization techniques. Springer, London (2012)CrossRefGoogle Scholar
  12. 12.
    Satapathy, S.C., Naik, A.: Data clustering based on teaching-learning-based optimization. In: Swarm, Evolutionary, and Memetic Computing. Lecture Notes in Computer Science, vol. 7077, pp. 148–156. Springer, Berlin (2011)Google Scholar
  13. 13.
    Satapathy, S.C., Naik, A., Parvathi, K.: High dimensional real parameter optimization with teaching learning based optimization. Int. J. Ind. Eng. Comput. (2012). doi: 10.5267/j.ijiec.2012.06.001 MATHGoogle Scholar
  14. 14.
    Satapathy, S.C., Naik, A., Parvathi, K.: Teaching learning based optimization for neural networks learning enhancement. In: LNCS, vol. 7677, pp. 761–769. Springer, Berlin (2012)Google Scholar
  15. 15.
    Satapathy, S.C., Naik, A., Parvathi, K.: 0–1 Integer Programming For Generation maintenance Scheduling in Power Systems based on Teaching Learning Based Optimization (TLBO), CCIS 306, pp. 53–63. Springer, Berlin (2012)Google Scholar
  16. 16.
    Satapathy, S.C., Naik, A., Parvathi, K.: Improvement of initial cluster center of c-means using Teaching learning based optimization. Elsevier, Procedia Technology 6(2012), 428–435 (2012)Google Scholar
  17. 17.
    Naik, A., Parvathi, K., Satapathy, S.C., Nayak, R., Panda, B.S.: QoS Multicast Routing Using Teaching Learning Based Optimization, pp. 49–55. Springer, Berlin (2012)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1. Department of ITGrietHyderabadIndia
  2. 2.Department of CS&SE, AUCE(a)Andhra UniversityVisakhapatnamIndia

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