A Gene Expression Data Biclustering Algorithm Using Large Average Submatrix Based Fcm Classification System
M. Ramkumar1, G. Nanthakumar2

1M. Ramkumar, Research Scholar, Sri Satya Sai University of Technology & Medical Sciences, (Madhya Pradesh), India.
2Dr. G. Nanthakumar, Associate Professor, Anjalai Ammal Mahalingam Engineering College, (Tamil Nadu), India.
Manuscript received on 05 June 2019 | Revised Manuscript received on 30 June 2019 | Manuscript Published on 04 July 2019 | PP: 753-756 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A11390681S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In this paper, the biclusters on data from gene expression tend to group or cluster identical data under multiple conditions on gene expression. Therefore, the biclustering method is very necessary if the matrix lines and columns are clustered instantaneously. At first, the set of sub-matrices are identified using Large Average Submatrix. This is based on a simple significance score which transcends the size and average value of a matrix. Large Average Submatrix is used in an iterative way, where a link between the maximum value and the minimum description length is established. With the total number of data from gene expression growing, the matrix will increase and the clustering problem will be deficient. In this stage, the use of the biclustering algorithm leads to severe problems as data is increased. We are therefore using Large Average Submatrix to improve the biclustering performance. This compresses or removes irrelevant or less correlated ones for improved clustering performance. We also use FCM to verify that for further calculation the number of rows and columns in the submatrix can be added. The method is calculated with regard to consistency of elements and submatrices capacity.
Keywords: Biclustering Algorithm, Gene Expression Data, FCM, Large Average Submatrix.
Scope of the Article: Classification