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ANT COLONY OPTIMIZATION BASED CLUSTERING ON GENE EXPRESSION DATASET

Abstract In this article, the gene expression biclusters appear to group or group similar gene expression data under different conditions. Therefore, if the matrix lines and columns are grouped instantaneously, the biclustering procedure is very important. The set of sub-matrices is first defined by a broad sub-matrix. The basis for this is a basic sense value that exceeds a matrix's size and value. Wide submatrix is used in an iterative fashion in which a relation is formed between the maximum value and the minimum definition length. The matrix will increase and the issue of grouping will be deficient as the overall amount of data from the gene expression will rise. The use of the biclustering algorithm creates major issues at this point as data is increased. We then use the broad submatrix to boost the efficiency of the biclustering. This compresses or excludes unrelated or less associated clustering output enhancements. We use ACO to check that the number of rows and columns can be applied to the sub-matrix for further estimation. The system is determined in terms of the uniformity of elements and capacity of the submatrices. Authors M Ramkumar Gnanamani College of Technology, India Keywords Biclustering Algorithm, Ant Colony Optimization, Gene Expression

Published By : ICTACT Published In : ICTACT Journal on Data Science and Machine Learning ( Volume: 1 , Issue: 3 , Pages: 81-83 ) Date of Publication : June 2020