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
收藏
页码：231 / 235
页数：5
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