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In most gene expression data, the number of training samples is very smallcompared to the large number of genes involved in the experiments. However,among the large amount of genes, only a small fraction is effective forperforming a certain task. Furthermore, a small subset of genes is desirable indeveloping gene expression based diagnostic tools for delivering reliable andunderstandable results. With the gene selection results, the cost of biologicalexperiment and decision can be greatly reduced by analyzing only the markergenes. An important application of gene expression data in functional genomicsis to classify samples according to their gene expression profiles. Featureselection (FS) is a process which attempts to select more informative features.It is one of the important steps in knowledge discovery. Conventionalsupervised FS methods evaluate various feature subsets using an evaluationfunction or metric to select only those features which are related to thedecision classes of the data under consideration. This paper studies a featureselection method based on rough set theory. Further K-Means, Fuzzy C-Means(FCM) algorithm have implemented for the reduced feature set withoutconsidering class labels. Then the obtained results are compared with theoriginal class labels. Back Propagation Network (BPN) has also been used forclassification. Then the performance of K-Means, FCM, and BPN are analyzedthrough the confusion matrix. It is found that the BPN is performing wellcomparatively.

Verdict Accuracy of Quick Reduct Algorithm using Clustering and Classification Techniques for Gene Expression Data