TY - JOUR
T1 - Clustering gene expression data with kernel principal components
AU - Liu, Zhenqiu
AU - Chen, Dechang
AU - Bensmail, Halima
AU - Xu, Ying
PY - 2005/4
Y1 - 2005/4
N2 - 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.
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
UR - http://www.scopus.com/inward/record.url?scp=17644400681&partnerID=8YFLogxK
U2 - 10.1142/S0219720005001168
DO - 10.1142/S0219720005001168
M3 - Article
C2 - 15852507
AN - SCOPUS:17644400681
SN - 0219-7200
VL - 3
SP - 303
EP - 316
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
IS - 2
ER -