title: Fractal dimension and wavelet decomposition for robust microarray data clustering.
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.
authors.affiliation: Mobile Information and Network Technologies Research Centre (MINT), Kingston University, London KT1 2EE, UK. r.istepanian@kingston.ac.uk
authors.forename: Robert S H
authors.initials: RS
authors.lastname: Istepanian
authors.forename: Ala
authors.initials: A
authors.lastname: Sungoor
authors.forename: Jean-Christophe
authors.initials: JC
authors.lastname: Nebel
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003201:Computers; D017709:Fractals; D005822:Genetic Vectors; D016678:Genome; D023281:Genomics / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software