Supervised Attribute Based Classification of Micro Array Samples

For analyzing gene expression data there is a need in use of clustering Technique. Clustering provides alternative approach for heuristic for probability analysis. Classification and clustering are the two important ones in gene expression data analysis. Classification is concerned with assigning memberships to samples based on expression patterns, and clustering aims at finding new biological classes and refining existing ones. To cluster and recognize patterns in gene expression datasets, dimension problems are encountered. Typically, gene expression datasets consist of a large number of genes (attributes) but a small number of samples (tuples). In real data analysis, one of the important issues is computing both relevance and redundancy of attributes by discover the dependencies among them. Selection of genes is important in the clustering technique. So here it implements attribute clustering method which is able to group genes based on their interdependence so that meaningful patterns can be formed from the gene expression data. Grouping and selection are two important factors. We implement it by gene expression data so that meaningful clusters were formed.