title: Fuzzy C-means method with empirical mode decomposition for clustering microarray data.
abstract: Microarray techniques have revolutionised genomic research by making it possible to monitor the expression of thousands of genes in parallel. The Fuzzy C-Means (FCM) method is an efficient clustering approach devised for microarray data analysis. However, microarray data contains noise, which would affect clustering results. In this paper, we propose to combine the FCM method with the Empirical Mode Decomposition (EMD) for clustering microarray data to reduce the effect of the noise. The results suggest the clustering structures of denoised microarray data are more reasonable and genes have tighter association with their clusters than those using FCM only.
authors.affiliation: Discipline of Mathematical Sciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane Q4001, Australia. yanfei.wang@student.qut.edu.au
authors.forename: Yan-Fei
authors.initials: YF
authors.lastname: Wang
authors.forename: Zu-Guo
authors.initials: ZG
authors.lastname: Yu
authors.forename: Vo
authors.initials: V
authors.lastname: Anh
mesh_terms: D016000:Cluster Analysis; D017143:Fuzzy Logic; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*