Recent advances in DNA microarray technology helps in obtaining gene expression profiles of tissue samples at fairly low costs. The amount of biological data such as DNA sequences and microarray data have been increased tremendously. DNA microarrays are emerged as the leading technology to measure gene expression levels primarily, because of their high throughput. Cluster analysis of gene expression data has proved to be a useful tool for identifying co-expressed genes. Information retrieval and data mining are the powerful tools to extract information from the databases and/or information repositories. The integrative cluster analysis of both clinical and gene expression data has shown to be an effective alternative to overcome problems such as less clustering accuracy, higher clustering time etc. There have been quite a few approaches proposed for the gene expression techniques. This work presents a brief survey of different clustering approaches of gene expression data techniques and relative study of these techniques. In this paper an analysis of different techniques used for gene expression data has been made. Finally, a suitable clustering technique has been suggested.
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