Clustering Techniques Analysis for Microarray Data?

Microarray data is gene expression data which consists of the protein level of various genes for some samples. It is a high dimensional data. High dimensionality is a curse for the analysis of gene expression data. Thus gene selection process is used in which most informative genes are selected from the pool of gene expression data set. All the genes are not relevant in each case. First we need to select those genes which are relevant as well as there should be least redundancy among them. For this purpose various approaches can be used such as: Filter methods, wrapper methods, embedded approach and clustering. In this paper embedded approach for gene selection and clustering method will be used for performing the sample clustering to refine the classification and will be compared with each other on the basis of various parameters.