An Improved Firefly Fuzzy C-Means (FAFCM) Algorithm for Clustering Real World Data Sets

  • Janmenjoy NayakEmail author
  • Matrupallab Nanda
  • Kamlesh Nayak
  • Bighnaraj Naik
  • Himansu Sekhar Behera
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Fuzzy c-means has been widely used in clustering many real world datasets used for decision making process. But sometimes Fuzzy c-means (FCM) algorithm generally gets trapped in the local optima and is highly sensitive to initialization. Firefly algorithm (FA) is a well known, popular metaheuristic algorithm that simulates through the flashing characteristics of fireflies and can be used to resolve the shortcomings of Fuzzy c-means algorithm. In this paper, first a firefly based fuzzy c-means clustering and then an improved firefly based fuzzy c-means algorithm (FAFCM) has been proposed and their performance are being compared with fuzzy c-means and PSO algorithm. The experimental results divulge that the proposed improved FAFCM method performs better and quite effective for clustering real world datasets than FAFCM, FCM and PSO, as it avoids to stuck in local optima and leads to faster convergence.


Clustering Fuzzy c-means Firefly Optimization PSO 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Izakian, H., Abraham, A.: Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Systems with Applications 38, 1835–1838 (2011)CrossRefGoogle Scholar
  2. 2.
    Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms, pp. 95–107. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  3. 3.
    Li, L., Liu, X., Xu, M.: A Novel Fuzzy Clustering Based on Particle Swarm Optimization. In: First IEEE International Symposium on Information Technologies and Applications in Education, pp. 88–90 (2007)Google Scholar
  4. 4.
    Wang, L., et al.: Particle Swarm Optimization for Fuzzy c-Means Clustering. In: Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China (2006)Google Scholar
  5. 5.
    Yang, X.S.: Nature-Inspired Metaheuristic Algorithms. Luniver Press (2008)Google Scholar
  6. 6.
    Senthilnath, J., Omkar, S.N., Mani, V.: Clustering using firefly algorithm: Performance study. Swarm and Evolutionary Computation 1, 164–171 (2011)CrossRefGoogle Scholar
  7. 7.
    Runkler, T.A., Katz, C.: Fuzzy Clustering by Particle Swarm Optimization. In: Proceedings of 2006 IEEE International Conference on Fuzzy Systems, Canada, pp. 601–608 (2006)Google Scholar
  8. 8.
    Zadeh, T.H., Meybodi, M.: A New Hybrid Approach for Data Clustering using Firefly Algorithm and K-means. In: 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISI), Fars, pp. 007-011 (2012)Google Scholar
  9. 9.
    Abshouri, A.A., Bakhtiary, A.: A New Clustering Method Based on Firefly and KHM. Journal of Communication and Computer 9, 387–391 (2012)Google Scholar
  10. 10.
    Yang, X.-S.: Firefly Algorithms for Multimodal Optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Yang, X.S.: Firefly Algorithm, Stochastic Test Functions and Design optimization. International Journal of Bio-Inspired Computation 2, 78–84 (2010)CrossRefGoogle Scholar
  12. 12.
    Yang, F., Sun, T., Zhang, C.: An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization. Expert Systems with Applications 36, 9847–9852 (2009)CrossRefGoogle Scholar
  13. 13.
    Niknam, T., Amiri, B.: An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis. Applied Soft Computing 10, 183–197 (2010)CrossRefGoogle Scholar
  14. 14.
    Huang, K.Y.: A hybrid particle swarm optimization approach for clustering and classification of datasets. Knowledge-Based Systems 24, 420–426 (2011)CrossRefGoogle Scholar
  15. 15.
    Chakravarty, S., Dash, P.K.: A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices. Applied Soft Computing 12(2), 931–941 (2012)CrossRefGoogle Scholar
  16. 16.
    Shayeghi, H., Jalili, A., Shayanfar, H.A.: Multi-stage fuzzy load frequency control using PSO. Energy Conversion and Management 49(10), 2570–2580 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Janmenjoy Nayak
    • 1
    Email author
  • Matrupallab Nanda
    • 1
  • Kamlesh Nayak
    • 1
  • Bighnaraj Naik
    • 1
  • Himansu Sekhar Behera
    • 1
  1. 1.Department of Computer Science & EngineeringVeer Surendra Sai University of Technology (VSSUT)BurlaIndia

Personalised recommendations