Vector Quantization of Microarray Gene Expression Data

被引：0

作者：

Prasad, T. V. ^{[1
]}

Kohli, Maitrei ^{[2
]}

机构：

[1] Lingayas Univ, Faridabad, Haryana, India

[2] Lingayas Univ, Dept Comp Sci & Engn, Faridabad, Haryana, India

来源：

WORLD CONGRESS ON ENGINEERING, WCE 2010, VOL I | 2010年

关键词：

Data mining; microarray gene expression data; artificial neural networks; vector quantization; clustering; classification;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A series of experiments conducted on various gene expression datasets revealed that the learning vector quantization (LVQ) produced better grouping of genes compared to other known efficient techniques such as self-organizing maps. The LVQ algorithms exhibited consistency and better accuracy compared to other clustering techniques such as SOM, HC, k-means, etc

引用

页码：231 / 235

页数：5