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Reseach Article

Analysis of Gene Expression Microarray Dataset for Feature Selection

Published on November 2012 by G. Baskar, P. Ponmuthuramalingam
National Conference on Communication Technologies & its impact on Next Generation Computing 2012
Foundation of Computer Science USA
CTNGC - Number 3
November 2012
Authors: G. Baskar, P. Ponmuthuramalingam
430a079d-318b-4d6c-87a1-9db126ee3c5e

G. Baskar, P. Ponmuthuramalingam . Analysis of Gene Expression Microarray Dataset for Feature Selection. National Conference on Communication Technologies & its impact on Next Generation Computing 2012. CTNGC, 3 (November 2012), 33-35.

@article{
author = { G. Baskar, P. Ponmuthuramalingam },
title = { Analysis of Gene Expression Microarray Dataset for Feature Selection },
journal = { National Conference on Communication Technologies & its impact on Next Generation Computing 2012 },
issue_date = { November 2012 },
volume = { CTNGC },
number = { 3 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 33-35 },
numpages = 3,
url = { /proceedings/ctngc/number3/9068-1032/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Communication Technologies & its impact on Next Generation Computing 2012
%A G. Baskar
%A P. Ponmuthuramalingam
%T Analysis of Gene Expression Microarray Dataset for Feature Selection
%J National Conference on Communication Technologies & its impact on Next Generation Computing 2012
%@ 0975-8887
%V CTNGC
%N 3
%P 33-35
%D 2012
%I International Journal of Computer Applications
Abstract

Microarray is a powerful technology for biological exploration which enables to simultaneously measure the level of activity of thousands genes in various cancer study . clustering is important data mining technique to extract useful information from various high dimensional datasets. A wide range of clustering algorithm is available and still in an open area of research k-Means algorithm is one of the basic and most simple partitioning clustering technique is given by Mac Queen in 1967. In this paper a sample weighting and efficient margin based sample weighting algorithm to improve the stability of feature selection. We proposed a weighted k-means to improve the cluster stability and presented an experimental evaluation of the proposed method, the experiment of microarray dataset show the feature selection algorithm such as SVM-RFE are more stable in gene selection.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Feature Selection Classification Clustering Gene Expression Microarray