Effective kernel-based possibilistic fuzzy clustering techniques: analyzing cancer database

  • S. R. KannanEmail author
  • M. Siva
  • R. Devi
  • S. Ramathilagam
  • Mark Last


This paper aims to present optimal clustering techniques for analyzing high-dimensional cancer databases with missing attributes and overlapped objects. Analyzing the high-dimensional database with missing values is considered as most difficult task, and so far, there is no optimal cluster technique available for clustering the cancer database. Therefore, this paper develops the effective fuzzy clustering techniques that incorporate Cauchy kernel induced distance, rudimentary centroids, possibilistic memberships, fuzzy memberships, and prototype equation. To reduce the computing time of algorithms, this paper introduces a method for finding reasonable initial cluster centers. Experimental results indicate that the proposed methods are suitable for the breast cancer databases with missing attributes, and the results indicate that the methods outperform in clustering the databases into available subclasses.


Clustering Fuzzy C-means Kernel distance High-dimensional databases Gene expression database 



This work was financially supported by DST India and MOST Israel.


  1. 1.
    J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, New York (Plenum Press, 1981)Google Scholar
  2. 2.
    B. Liu, C. Wan, L.P. Wang, An efficient semi-unsupervised gene selection method via spectral biclustering. IEEE Trans. Nano-Biosci. 5(2), 110–114 (2006)CrossRefGoogle Scholar
  3. 3.
    C. Alzate, J.A.K. Suykens, Sparse kernel spectral clustering models for large-scale data analysis. Neurocomputing 74(9), 1382–1390 (2011)CrossRefGoogle Scholar
  4. 4.
    C.-H. Wang, Outlier identification and market segmentation using kernel-based clustering techniques. Expert Syst. Appl. 36, 3744–3750 (2009)CrossRefGoogle Scholar
  5. 5.
    C.-H. Lai et al., Oncogenes and subtypes of diffuse large B-cell lymphoma discoveries from microarray database. (JCIS, Atlantis Press, 2006)Google Scholar
  6. 6.
    R.G. Congalton, K. Green, Assessing the accuracy of remotely sensed data: principles and practices (Lewis Publishers, USA, 1992)Google Scholar
  7. 7.
    F. Masulli et al., A fuzzy clustering based segmentation system as support to diagnosis in medical imaging. Artif. Intell. Med. 16, 129–147 (1999)CrossRefGoogle Scholar
  8. 8.
    H. Shen, J. Yang, S. Wang, X. Liu, Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets. Soft. Comput. 10, 1061–1073 (2006)CrossRefGoogle Scholar
  9. 9.
    H. Zhang, G. Yu, A novel clustering and mining algorithm for high dimensional data based on uncertainty criteria and fuzzy mathematics. Rev. Téc. Ing. Univ. Zulia 39(2), 1–11 (2016)Google Scholar
  10. 10.
    H. Yang, N.J. Pizzi, Biomedical data classification using hierarchical clustering. Proc. IEEE Can. Conf. Elect. Comput. Eng, Niagara Falls 4, 1861–1864 (2004)Google Scholar
  11. 11.
    M. Jezewski, An application of modified fuzzy clustering to medical data classification. J. Med. Inf. Technol. 17, 51–57 (2011)Google Scholar
  12. 12.
    S.R. Kannan, M. Siva, S. Ramathilagam, R. Devi, Effective kernel based fuzzy clustering systems in analyzing cancer database. Data-Enabled Discov. Appl. 2(1), 5 (2018)CrossRefGoogle Scholar
  13. 13.
    L. Bai, J. Liang, An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data. Knowl.-Based Syst. 24(6), 785–795 (2011)CrossRefGoogle Scholar
  14. 14.
    R.S. Lunetta et al., Remote sensing and geographic information system data integration: Error sources and research issues. Photogramm. Eng. Remote. Sens. 57, 677–687 (1991)Google Scholar
  15. 15.
    M.A. Bakhshali, Segmentation and enhancement of brain MR images using fuzzy clustering based on information theory. Soft. Comput. 21(22), 6633–6640 (2017)CrossRefGoogle Scholar
  16. 16.
    N.S. Mishra, S. Ghosh, A. Ghosh, Fuzzy clustering algorithms incorporating local information for change detection in remotely sensed images. Appl. Soft Comput. 12, 2683–2692 (2012)CrossRefGoogle Scholar
  17. 17.
    R. Winkler, F. Klawonn, R. Kruse, Fuzzy C-means in high dimensional spaces. Int. J. Fuzzy Syst. Appl. 1(1), 1–16 (2011) [15]CrossRefGoogle Scholar
  18. 18.
    R.-H. Lin, An Intelligent model for liver disease diagnosis. Artif. Intell. Med. 47(1), 53–62 (2009)CrossRefGoogle Scholar
  19. 19.
    P.J. Rousseeuw, Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)CrossRefGoogle Scholar
  20. 20.
    S. Saheb Basha, Satya Prasad, Automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy c-means clustering. J. Theor. Appl. Inf. Technol. 5(6), 704–709 (2009)Google Scholar
  21. 21.
    S. Ben-David, N. Haghtalab, Clustering in the presence of background noise, Proceedings of the 31st International Conference on Machine Learning. PMLR 32(2), 280–288 (2014)Google Scholar
  22. 22.
    S.D. Mai, L.T. Ngo, Multiple kernel approach to semi-supervised fuzzy clustering algorithm for land-cover classification. Eng. Appl. Artif. Intell. 68, 205–213 (2018)CrossRefGoogle Scholar
  23. 23.
    S. Ghosh et al., A novel neuro-fuzzy classification technique for data mining. Egyp. Inf. J. 15(3), 129–147 (2014)Google Scholar
  24. 24.
    P. Tamayo et al., Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. U. S. A. 96(6), 2907 (1999)CrossRefGoogle Scholar
  25. 25.
    S. Tavazoie, J.D. Hughes, M.J. Campbell, R.J. Cho, G.M. Church, Systematic determination of genetic network architecture. Nat. Genet. 22, 281–285 (1999)CrossRefGoogle Scholar
  26. 26.
    D. Vanisri, C. Loganathan, An efficient fuzzy possibilistic C-means with penalized and compensated constraints. Global J. Comp. Sci. Technol. 11(1), (2011)Google Scholar
  27. 27.
    X. Chang, Q. Wang, Y. Liu, Y. Wang, Sparse regularization in fuzzy c-means for high-dimensional data clustering. IEEE Trans. Knowl. Data Eng. 47(9), 2616–2627 (2017)Google Scholar
  28. 28.
    R. Xu, S. Damelin, B. Nadler, D.C. Wunsch II, Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps. Artif. Intell. Med. 48(2–3), 91–98 (2010)CrossRefGoogle Scholar
  29. 29.
    Y. Ding, X. Fu, Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing 188, 233–238 (2016)CrossRefGoogle Scholar
  30. 30.
    E.A. Zanaty, S. Aljahdali, N.A. Debnath, Kernelized fuzzy c-means algorithm for automatic magnetic resonance image segmentation. J. Comput. Methods Sci. Eng. 9, 123–136 (2009)zbMATHGoogle Scholar
  31. 31.
    X.-q. Zhao, J.-h. Zhou, Improved kernel possibilistic fuzzy clustering algorithm based on invasive weed optimization. J. Shanghai Jiaotong Univ. (Sci.) 20(2), 164–170 (2015)MathSciNetCrossRefGoogle Scholar
  32. 32.
    X. Zhao, Y. Li, Q. Zhao, A fuzzy clustering approach for complex color image segmentation based on Gaussian model with interactions between color planes and mixture Gaussian model. Int. J. Fuzzy Syst. 20(1), 309–317 (2018)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Y. Zheng, B. Jeon, D. Xu, et al., Image segmentation by generalized hierarchical fuzzy c-means algorithm. J. Intell. Fuzzy Syst. 28(2), 961–973 (2015)Google Scholar
  34. 34.
    D. Zhou, H. Zhou, A modified strategy of fuzzy clustering algorithm for image segmentation. Soft. Comput. 19(11), 3261–3272Google Scholar
  35. 35.
    UCI Machine Learning Repository, University of California (School of Information and Computer Science, Irvine, 2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • S. R. Kannan
    • 1
    Email author
  • M. Siva
    • 1
  • R. Devi
    • 2
  • S. Ramathilagam
    • 3
  • Mark Last
    • 4
  1. 1.Pondicherry University (A Central University of India)PondicherryIndia
  2. 2.Pachaiyappa’s College for MenChennaiIndia
  3. 3.Periyar Govt. Arts CollegeCuddaloreIndia
  4. 4.Ben-Gurion University of the NegevBeershebaIsrael

Personalised recommendations