A Novel Attribute Clustering Algorithm for Extraction of Discriminative Features to Classify Samples from Microarray Gene Expression Data

High dimensionality of microarray gene expression data has been a major problem in gene array- based sample classification. However, it is very difficult to identify marker genes for disease diagnosis. In this regard, a new partition based attribute clustering algorithm is proposed to cluster genes from microarray data. The proposed method directly incorporates the information of response variables in the grouping process for finding such groups of genes, yielding a supervised clustering algorithm for genes. Some significant cluster representatives are then taken to form the reduced feature set that can be used to build the classifiers with very high classification accuracy. The performance of the proposed method is described based on the predictive accuracy of naive bayes classifier, K- nearest neighbor rule, and support vector machine. The effectiveness of the proposed method, along with a comparison with existing methods, is demonstrated on different microarray data sets. 