Microarray experiments produce expression profiles measured under some experimental conditions and are normally labeled on the basis of information such as, clinical identification of tissue samples or expression of genes with respect to time. The comparisons of different samples which render accurate information about over-expression/under-expression of genes provide significant clues in understanding the mechanism of the underlying phenomenon (for example, disease, pathways etc.). While microarray technology provides a breakthrough in computational genomics but the knowledge discovery from microarray is still at its infancy. The primary focus of this poster is two-way fuzzy clustering of microarray data using adaptive subspace iteration (Fuzzy-ASI) based algorithm for finding differentially expressed genes (DEGs) from two-sample microarray experiments. The proposed Fuzzy-ASI assigns a relevance value to each gene associated with each cluster using a progressive clustering framework. The functional categories are ranked based on their potential to classify sample classes correctly. The high ranked clusters are indicative of DEGs. Empirical analyses on simulated, 100 artificial microarray datasets are used to quantify the results obtained using the Fuzzy-ASI algorithm. Further analyses on different microarray cancer datasets revealed several important genes that are relevant with various cancers. A three fold validation is performed on the DEGs otained using the fuzzy-ASI based two-way clustering technique. First, comparison of the DEGs is performed with the DEGs obtained by the original authors. Secondly, the relevance of DEGs to the disease under study is assessed by referring to the literature. Finally, a 3D star coordinate projection algorithm (3D SCP) is used for visual validation. The visual results using principal component analysis and heatmap are also provided to complement the 3D SCP results.