Fuzzy C-means method with empirical mode decomposition for clustering microarray data

Authors:
Yan-Fei Wang;Zu-Guo Yu;Vo Anh
Affiliations:
Discipline of Mathematical Sciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane Q4001, Australia;Discipline of Mathematical Sciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane Q4001, Australia/ School of Mathematics and Computational Science, Xiangtan Un ...;Discipline of Mathematical Sciences, Faculty of Science and Technology, Queensland University of Technology, Brisbane Q4001, Australia
Venue:
International Journal of Data Mining and Bioinformatics
Year:
2013

Citing 6
Cited 0

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Journal of Computational and Applied Mathematics
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Functional Similarity Analyzing of Protein Sequences with Empirical Mode Decomposition

FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications

International Journal of Data Mining and Bioinformatics
WF-MSB: A weighted fuzzy-based biclustering method for gene expression data

International Journal of Data Mining and Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

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.