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

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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.

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Acknowledgments

This work was supported by Indo Taiwan Joint Research Project, DST India & NSC Taiwan.

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Authors and Affiliations

  1. Department of Mathematics, Pondicherry University, Pondicherry, India

    S. R. Kannan & R. Devi

  2. Department of Mathematics, Periyar Government College, Cuddalore, India

    S. Ramathilagam

  3. NUK, Kaohsiung, Taiwan

    T. P Hong

Authors
  1. S. R. Kannan
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  2. R. Devi
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  3. S. Ramathilagam
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  4. T. P Hong
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Corresponding author

Correspondence to S. R. Kannan.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by C.-H. Chen.

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Kannan, S.R., Devi, R., Ramathilagam, S. et al. Effective fuzzy possibilistic c-means: an analyzing cancer medical database. Soft Comput 21, 2835–2845 (2017). https://doi.org/10.1007/s00500-016-2198-7

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  • DOI: https://doi.org/10.1007/s00500-016-2198-7

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