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Weighted kernel fuzzy c-means method for gene expression analysis

conference contribution
posted on 2012-01-01, 00:00 authored by Y Wang, Maia Angelova TurkedjievaMaia Angelova Turkedjieva
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.

History

Event

IEEE Xi'an Section. Conference (2012 : Xi'an, China)

Series

IEEE Xi'an Section Conference

Pagination

1 - 4

Publisher

Institute of Electrical and Electronics Engineers

Location

Xi'an, China

Place of publication

Piscataway, N.J.

Start date

2012-05-27

End date

2012-05-30

ISBN-13

9781457719646

Language

eng

Publication classification

E Conference publication; E1.1 Full written paper - refereed

Copyright notice

2012, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

S-CET 2012 : Sharing, cooperating and improving : Proceedings of the 2012 Spring Congress on Engineering and Technology

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