Clustering gene expression data with kernel principal components

Zhenqiu Liu*, Dechang Chen, Halima Bensmail, Ying Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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
StatePublished - Apr 2005
Externally publishedYes


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


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