An Efficient Artificial Bee Colony and Fuzzy C Means Based Clustering Gene Expression Data

Provided by: The International Journal of Innovative Research in Computer and Communication Engineering
Topic: Data Management
Format: PDF
Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes as it partition a given data set into groups based on particular features. The gene microarray data are arranged based on the pattern of gene expression using various clustering algorithms and the dynamic natures of biological processes are generally 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 of microarray gene expression data is carried out using Locality Sensitive Discriminant Analysis (LSDA).

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