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
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DOI: 10.1007/s00500-016-2198-7

Cite this article as:
Kannan, S.R., Devi, R., Ramathilagam, S. et al. Soft Comput (2017) 21: 2835. doi:10.1007/s00500-016-2198-7
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Abstract

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.

Keywords

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

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

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