期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2013
卷号：1
期号：6
出版社：S&S Publications
摘要：Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genesas it partition a given data set into groups based on particular features. The gene microarray data are arranged based onthe pattern of gene expression using various clustering algorithms and the dynamic natures of biological processes aregenerally unnoticed by the traditional clustering algorithms. To overcome the problems in gene expression analysis,novel algorithms for dimensionality reduction and clustering have been proposed. The dimensionality reduction ofmicroarray gene expression data is carried out using Locality Sensitive Discriminant Analysis (LSDA). To maintainbond between the neighborhoods in locality, LSDA is used and an efficient metaheuristic optimization algorithm calledArtificial Bee Colony (ABC) using Fuzzy c Means clustering is used for clustering the gene expression based on thepattern. The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes lesserless clustering time when compared with existing algorithms.
关键词：Gene expression data; Locality Sensitive Discriminant Analysis; Artificial Bee Colony; Fuzzy c Means
