Fractal dimension and wavelet decomposition for robust microarray data clustering

Istepanian, Robert S.H., Sungoor, Ala and Nebel, Jean-Christophe (2008) Fractal dimension and wavelet decomposition for robust microarray data clustering. In: 30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society; 20 - 24 Aug 2008, Vancouver, Canada. ISSN (print) 1557-170X

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Abstract

Microarrays are now established technologies which are considered as key to gene expression analysis. Their study is usually achieved by using clustering techniques. Genomic signal processing is a new area of research that combines genomics with digital signal processing methodologies. In this paper, we present a comparative analysis of two genomic signal processing methods for robust microarray data clustering. Techniques based on Fractal Dimension and Discrete Wavelet Decomposition with Vector Quantization are validated for standard data sets. Comparative analysis of the results indicates that these methods provide improved clustering accuracy compared to some conventional clustering techniques. Moreover, these classifiers don't require any prior training procedures.

Item Type: Conference or Workshop Item (Paper)
Event Title: 30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
Uncontrolled Keywords: gene-expression data, component analysis, tumor classification
Research Area: Computer science and informatics
Physics
Faculty, School or Research Centre: Faculty of Computing, Information Systems and Mathematics (until 2011)
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Depositing User: Automatic Import Agent
Date Deposited: 26 May 2010 10:41
Last Modified: 26 May 2010 10:41
URI: http://eprints.kingston.ac.uk/id/eprint/12646

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