An Encapsulated Approach for Microarray Sample Classification using Supervised Attribute clustering and Fuzzy Classification Algorithm

ABSTRACT Microarray classification technique is one of the important biotechnology used to record thousands of genes simultaneously within a number of different samples. Among the great amount of genes presented in microarray gene expression data, only a small amount of gene is effective for performing a certain diagnostic test. In order to find the effective group of genes, a supervised attribute Clustering Algorithm is introduced in this paper. In this regard, mutual information has been shown to be successful for selecting a set of relevant and non-redundant genes from microarray data. To reduce the redundancy among the attributes, a new quantitative measure based on mutual information is used, which incorporate the information into sample categories. It implement SAC for grouping co regulated genes within strong association to class labels.SAC forms cluster based on the similarity measures which are more effective when compared with the existing algorithm. The growth of the cluster is repeated until the cluster gets stabilized. Finally classify the selected gene set using Fuzzy Classification algorithm. The results are more accurate than the normal classification algorithms.