Volume: 3, Issue: 2(2005)
pp. 303-316 DOI: 10.1142/S0219720005001168
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Abstract |
Full Text (PDF, 1,026KB)
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References
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| Title: |
CLUSTERING GENE EXPRESSION DATA WITH KERNEL PRINCIPAL COMPONENTS |
| Author(s): |
ZHENQIU LIU
Bioinformatics Cell, TATRC, 110 North Market Street, Frederick, MD 21701, USADECHANG CHEN
Department of Preventive Medicine and Biometrics, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd, Bethesda, MD 20814, USAHALIMA BENSMAIL
Department of Statistics, University of Tennessee, 334 Stokely Management Center, Knoxville, TN 37996, USAYING XU
Department of Biochemistry and Molecular Biology, University of Georgia, 120 Green Street, Athens, GA 30602, USA
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| History: |
Received 2 June 2004
Accepted 20 July 2004
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| 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. |
| Keywords: |
Fuzzy C-means; kernel principal component analysis; microarray experiment; unsupervised learning
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