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

  • S. R. Kannan
  • M. Siva
  • R. Devi
  • S. Ramathilagam
  • Mark Last
APPLICATION
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Abstract

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.

Keywords

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

Notes

Funding

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

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • S. R. Kannan
    • 1
  • 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

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