BibTeX:

@article{IJIRSTV3I5026,
     title={A Survey on Biclustering},
     author={Shruthi. M. P and Saravana Kumar. E},
     journal={International Journal for Innovative Research in Science & Technology},
     volume={3},
     number={5},
     pages={192--194},
     year={},
     url={http://www.ijirst.org/articles/IJIRSTV3I5026.pdf},
     publisher={IJIRST (International Journal for Innovative Research in Science & Technology)},
}

A variety of clustering approaches are used for the analysis of gene expression obtained from microarray experiments. However, the results are limited when using standard clustering methods. These results are obligatory by the existence of various experimental conditions where the activity of the genes is unrelated. The same limitation exists when traditional clustering algorithms is performed. For this reason, a number of algorithms which simultaneously clusters the rows and columns in a gene expression matrix. This simultaneous clustering, usually called as biclustering which finds the subgroups of genes and subgroups of columns, where genes exhibit correlated activities for each and every condition. This type of biclustering algorithms were used in many fields such as information retrieval and data mining. This paper analyses a large number of biclustering algorithms, used for mining gene expression. It also classifies the genes in accordance with the type of biclusters they can find and used to perform the search and also the target applications.

Clustering, Biclustering, Microarray Data or Gene Expression Data