Analysis of Gene Expression Microarray Dataset for Feature Selection
G Baskar and P Ponmuthuramalingam. Article: Analysis of Gene Expression Microarray Dataset for Feature Selection. IJCA Proceedings on National Conference on Communication Technologies & its impact on Next Generation Computing 2012 CTNGC(3):33-35, November 2012. Full text available. BibTeX
@article{key:article,
author = {G. Baskar and P. Ponmuthuramalingam},
title = {Article: Analysis of Gene Expression Microarray Dataset for Feature Selection},
journal = {IJCA Proceedings on National Conference on Communication Technologies & its impact on Next Generation Computing 2012},
year = {2012},
volume = {CTNGC},
number = {3},
pages = {33-35},
month = {November},
note = {Full text available}
}
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.
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