SURVEY ON BICLUSTERING AND ITS APPLICATIONS

Abstract

Biclustering is the process of immediate taking apart of the set of samples and the set of their attributes into classes. Samples and attributes are classiï¬ed together which is believed to have a high importance to each other. Though, the outcome of the application of classic clustering methods to genes is limited. These limited results are forced by the survival of a number of investigational conditions where the activity of genes is not correlated. For this purpose, a number of algorithms that perform real-time clustering of the expression matrix have been proposed. In this survey, analysis about the most widely used biclustering techniques and their associated applications regarding various fields. This survey presents an study of several biclustering algorithms proposed by various authors to deals with the gene expression data efficiently. The existing algorithms are analyzed thoroughly to identify their advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. Then, in order to improve the performance of the best approach, a novel approach is been proposed in this paper.