Penalty term based suitable fuzzy intuitionistic possibilistic clustering: analyzing high dimensional gene expression cancer database

S. R. Kannan, Esha Kashyap, Mark Last, Tzung Pei Hong

Research output: Contribution to journalArticlepeer-review

Abstract

The aim of this paper is to identify the co-expressed potential genes that may serve for the development of the portions of normal or tumor. This paper differentiates the co-expressed genes into normal samples and tumor samples from gene expression dataset GSE25066. Since the dataset has vague boundaries and having common characteristics between the clusters, identifying the subgroups contain similar gene expression is really a tricky task one. Therefore, this paper introduces an effective fuzzy iterative clustering algorithm by incorporating kernel function, possibilistic c-means, fuzzy memberships, neighborhood information, median of neighboring objects and penalty term. The performances of the proposed clustering techniques have been shown through the succession experimental works on GSE25066. The effects of clustering results have been proved through comparing the resulted classes with ground truth.

Original languageEnglish
Pages (from-to)9839-9857
Number of pages19
JournalSoft Computing
Volume25
Issue number15
DOIs
StatePublished - 1 Aug 2021

Keywords

  • Big data
  • Cancer database
  • Fuzzy clustering
  • Neighboring objects
  • Penalty term

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