Advertisement

Comparison of Different Fuzzy Clustering Algorithms: A Replicated Case Study

  • Tusharika Singh
  • Anjana Gosain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

Fuzzy clustering partitions data points of a dataset into clusters in which one data point can belong to more than one cluster. In the literature, a number of fuzzy clustering algorithms have been proposed. This paper reviews various fuzzy clustering algorithms such as Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), Possibilistic Fuzzy C-Means (PFCM), Intuitionistic Fuzzy C-Means (IFCM), Kernel Fuzzy C-Means (KFCM), and Density-Oriented Fuzzy C-Means (DOFCM). We have demonstrated the experimental performance of these algorithms on some standard and synthetic datasets which include—Bensaid, Square (DUNN), D15, and D45 dataset. Then, the results are analyzed and compared to see the effectiveness of these algorithms in presence of noise and outliers.

Keywords

Fuzzy clustering FCM PCM PFCM IFCM KFCM DOFCM Outliers 

References

  1. 1.
    Hung, C.C., Kulkarni, S., Kuo, B.: A new weighted fuzzy c-means clustering algorithm for remotely sensed image classification. IEEE J. Sel. Top. Signal Process. 5(3), 543–553 (2011)CrossRefGoogle Scholar
  2. 2.
    Grover, N.: A study of various fuzzy clustering algorithms. Int. J. Eng. Res. (IJER) 3(3), 177–181 (2014)CrossRefGoogle Scholar
  3. 3.
    Gosain, A., Dahiya, S.: Performance analysis of various fuzzy clustering algorithms: a review. Proc. Comput. Sci. 100–111 (2016)CrossRefGoogle Scholar
  4. 4.
    Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum (1981)CrossRefGoogle Scholar
  5. 5.
    Gong, M., et al.: Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process. 22(2), 573–584 (2013)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Sharma, S., Goel, M., Kaur, P.: Performance comparison of various robust data clustering algorithms. Int. J. Intell. Syst. Appl. 5(7), 63–71 (2013)Google Scholar
  7. 7.
    Zhang, D., Chen, S.C.: Kernel-based fuzzy and possibilistic c-means clustering. In: Proceedings of the International Conference Artificial Neural Network (2003)Google Scholar
  8. 8.
    Chaira, T.: A novel intuitionistic fuzzy c means clustering algorithm and its application to medical images. Appl. Soft Comput. 11, 1711–1717 (2011)CrossRefGoogle Scholar
  9. 9.
    Kaur, P., Lamba, I.M.S., Gosain, A.: DOFCM: a robust clustering technique based upon density. IACSIT Int. J. Eng. Technol. 3(3), 297–303 (2011)CrossRefGoogle Scholar
  10. 10.
    Krishnapuram, R., Keller, J.M.: The possibilistic c-means algorithm: insights and recommendations. IEEE Trans. Fuzzy Syst. 4(3), 385–393 (1996)CrossRefGoogle Scholar
  11. 11.
    Pal, N.R., et al.: A possibilistic fuzzy c-means clustering algorithm. IEEE Trans. Fuzzy Syst. 13(4), 517–530 (2005)CrossRefGoogle Scholar
  12. 12.
    Kaur, P., et al.: Novel intuitionistic fuzzy C-means clustering for linearly and nonlinearly separable data. WSEAS Trans. Comput. 11(3), 65–76 (2012)MathSciNetGoogle Scholar
  13. 13.
    Gosain, A., Singh, T.: DKFCM: kernelized approach to density-oriented clustering. In: Accepted in 4th International Conference on Computational Intelligence in Data Mining (ICCIDM-2017), Odisha, India, Nov 2017Google Scholar
  14. 14.
    Bensaid, A.M., et al.: Validity-guided (re) clustering with applications to image segmentation. IEEE Trans. Fuzzy Syst. 4(2), 112–123 (1996)CrossRefGoogle Scholar
  15. 15.
    Kaur, P., Soni, A.K., Gosain, A.: Robust kernelized approach to clustering by incorporating new distance measure. Eng. Appl. Artif. Intell. 26(2), 833–847 (2013)CrossRefGoogle Scholar
  16. 16.
    Rehm, F., Klawonn, F., Kruse, R.: A novel approach to noise clustering for outlier detection. Soft. Comput. 11(5), 489–494 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.University School of Information and Communication TechnologyGuru Gobind Singh Indraprastha UniversityDwarkaIndia

Personalised recommendations