Weighted kernel fuzzy c-means method for gene expression analysis

Wang, Yu and Angelova, Maia (2012) Weighted kernel fuzzy c-means method for gene expression analysis. In: Engineering and Technology (S-CET), 2012 Spring Congress on. IEEE. ISBN 978-1-4577-1965-3

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Official URL: http://dx.doi.org/10.1109/SCET.2012.6342018


Many clustering techniques have been proposed for the analysis of gene expression data. However, the optimal method for a given experimental dataset is still not resolved. Fuzzy c-means and kernel fuzzy c-means algorithm have been widely applied to gene expression data, but they give the equal weight to the genes and noises, which lead to results that are not stable or accurate. In this paper, we propose a local weighted fuzzy clustering method in the kernel space. The original data is mapped to the high-dimensional feature space and Gaussian function is employed to investigate the local information of the cluster centre. Consequently, it will assign different weights to the noise and genes. Our experiments show that the proposed methods achieve better clustering effect than the fuzzy clustering algorithm and fuzzy kernel clustering algorithm.

Item Type: Book Section
Uncontrolled Keywords: clustering, gene expression data, kernel function, noise
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Becky Skoyles
Date Deposited: 20 Jan 2015 14:04
Last Modified: 12 Oct 2019 22:25
URI: http://nrl.northumbria.ac.uk/id/eprint/20721

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