Soft Computing

, Volume 21, Issue 11, pp 2835–2845

Effective fuzzy possibilistic c-means: an analyzing cancer medical database

  • S. R. Kannan
  • R. Devi
  • S. Ramathilagam
  • T. P Hong


Using clustering analysis for identifying cancer types in high-dimensional microarray gene expression cancer database is extremely difficult task because of high-dimensionality gene with noise. Most of the existing clustering methods for microarray gene expression cancer database to achieve types of cancers often hamper the interpretability of the structure. Hence, this paper presents effective fuzzy c-means by incorporating the membership function of fuzzy c-means, the typicality of possibilistic c-means approaches, normed kernel-induced distance, to find cancer subtypes in the microarray gene expression cancer database. This paper successfully finds the subtypes of cancers in microarray gene expression cancer database using the proposed method. The superiority of the proposed method has been proved through clustering accuracy.


Clustering Fuzzy c-means Possibilistic c-means Medical database Colon cancer 


  1. Alon U (1999) Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA 96(12):6745–6750CrossRefGoogle Scholar
  2. Bezdek JC (1981) Pattern recognition with Fuzzy objective function algorithms. Plenum Press, New YorkCrossRefMATHGoogle Scholar
  3. Chen H-L, Yang B, Liu J, Liu D-Y (2011) A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst Appl 38:9014–9022CrossRefGoogle Scholar
  4. Coppi R et al (2012) Fuzzy and possibilistic clustering for fuzzy data. Comput Stat Data Anal 56(4):915–927MathSciNetCrossRefMATHGoogle Scholar
  5. De Bin R et al (2011) A novel approach to the clustering of microarray data via nonparametric density estimation. BMC Bioinform 12(1):49–56CrossRefGoogle Scholar
  6. Hartigan JA (1975) Clustering algorithms. Wiley, New YorkMATHGoogle Scholar
  7. Hawes SE et al (2010) DNA hypermethylation of tumors from non-small cell lung cancer (NSCLC) patients is associated with gender and histologic type. Lung Cancer 69:172–179CrossRefGoogle Scholar
  8. He Z, Xu X, Deng S (2005) A cluster ensemble method for clustering categorical data. Inf Fusion 6(2):143–151CrossRefGoogle Scholar
  9. Kanzawa Y, Endo Y, Miyamoto S (2008) Fuzzy classification function of entropy regularized fuzzy c-means algorithm for data with tolerance using kernel function. In: The 2008 IEEE International Conference on Granular Computing, pp 350–355Google Scholar
  10. Kashef R et al (2010) Cooperative clustering. Pattern Recognit 43:2315–2329CrossRefMATHGoogle Scholar
  11. Kumar S et al (2012) Development of an efficient clustering technique for colon dataset. Int J Eng Innov Technol 1(5):83–86Google Scholar
  12. Mclachlan GJ et al (2002) A mixture model based approach to the clustering of micro-array expression data. Bio Inform 18(3):413–422Google Scholar
  13. Parkin DM, Bray F, Ferlay J, Pisani P (2005) Global cancer statistics. CA Cancer J Clin 55(2):74–108CrossRefGoogle Scholar
  14. Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65CrossRefMATHGoogle Scholar
  15. Thomas R, Thieffry D, Kaufman M (1995) Dynamical behaviour of biological regulatory networks—I. Bull Math Biol 57:247–276 17CrossRefGoogle Scholar
  16. Tjhi W-C, Chen L (2007) Possibilistic fuzzy co-clustering of large document collections. Pattern Recognit 40(12):3452–3466CrossRefMATHGoogle Scholar
  17. Turm H et al (2014) Comprehensive analysis of transcription dynamics from brain samples following behavioral experience. J Vis Exp 90:e51642–e51642. doi:10.3791/51642 Google Scholar
  18. Vanisri D et al (2011) An efficient fuzzy possibilistic C-means with penalized and compensated constraints. Global J Comput Sci Technol 11(3):15–21Google Scholar
  19. Yu Z, Wong HS, Wang H (2007) Graph based consensus clustering for class discovery from gene expression data. Bioinformatics 23(21):2888–2896CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • S. R. Kannan
    • 1
  • R. Devi
    • 1
  • S. Ramathilagam
    • 2
  • T. P Hong
    • 3
  1. 1.Department of MathematicsPondicherry UniversityPondicherryIndia
  2. 2.Department of MathematicsPeriyar Government CollegeCuddaloreIndia
  3. 3.NUKKaohsiungTaiwan

Personalised recommendations