Evolutionary fuzzy clustering algorithm with knowledge-based evaluation and applications for gene expression profiling

Han Saem Park, Si Ho Yoo, Sung Bae Cho

Research output: Contribution to journalArticle

26 Citations (Scopus)


In microarray data analysis, clustering is a method that groups thousands of genes by their similarities of expression levels, helping to analyze gene expression profiles. This method has been used for identifying unknown functions of genes. The fuzzy clustering method assigns one sample to multiple groups according to their degrees of membership. This method is more appropriate for analyzing gene expression profiles, because a single gene might be involved in multiple functions. General clustering methods, however, have problems in that they are sensitive to initialization and can be trapped into local optima. To overcome these problems, we propose an evolutionary fuzzy clustering method with knowledge-based evaluation. The proposed method uses a genetic algorithm for clustering and prior knowledge of experimental data for evaluation. We have performed experiments to show the usefulness of the proposed method with yeast cell-cycle and SRBCT datasets.

Original languageEnglish
Pages (from-to)524-533
Number of pages10
JournalJournal of Computational and Theoretical Nanoscience
Issue number4
Publication statusPublished - 2005 Dec 1


All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Materials Science(all)
  • Condensed Matter Physics
  • Computational Mathematics
  • Electrical and Electronic Engineering

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