Clustering gene expression data with kernel principal components

Zhenqiu Liu; Dechang Chen; Halima Bensmail; Ying Xu

doi:10.1142/S0219720005001168

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 journal › Article › peer-review

9 Scopus citations

Abstract

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 language	English
Pages (from-to)	303-316
Number of pages	14
Journal	Journal of Bioinformatics and Computational Biology
Volume	3
Issue number	2
DOIs	https://doi.org/10.1142/S0219720005001168
State	Published - Apr 2005
Externally published	Yes

Keywords

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

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10.1142/S0219720005001168

Cite this

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title = "Clustering gene expression data with kernel principal components",

abstract = "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.",

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author = "Zhenqiu Liu and Dechang Chen and Halima Bensmail and Ying Xu",

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AU - Chen, Dechang

AU - Bensmail, Halima

AU - Xu, Ying

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AB - 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.

KW - Fuzzy C-means

KW - Kernel principal component analysis

KW - Microarray experiment

KW - Unsupervised learning

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EP - 316

JO - Journal of Bioinformatics and Computational Biology

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ER -

Clustering gene expression data with kernel principal components

Abstract

Keywords

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