Clustering gene expression data with kernel principal components

Zhenqiu Liu, Dechang Chen, Halima Bensmail, Ying Xu

Research output: Contribution to journalArticle

8 Citations (Scopus)


Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

Original languageEnglish
Pages (from-to)303-316
Number of pages14
JournalJournal of Bioinformatics and Computational Biology
Issue number2
Publication statusPublished - 1 Apr 2005



  • Fuzzy C-means
  • Kernel principal component analysis
  • Microarray experiment
  • Unsupervised learning

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications

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