  In Tri-clustering, a sub-matrix is being created, which exhibit highly
similar behavior with respect to genes, conditions and time-points. In this
technique, genes with same expression values are discovered across some
fragment of time points, under certain conditions. In this paper, triclustering
using evolutionary algorithm is implemented using a new fitness function
consisting of 3D Mean Square residue (MSR) and Least Square approximation
(LSL). The primary objective is to find triclusters with minimum overlapping,
low MSR, low LSL and covering almost every element of expression matrix, thus
minimizing the overall fitness value. To improve the results of algorithm, new
fitness function is introduced to find good quality triclusters. It is observed
from experiments that, triclustering using EA yielded good quality triclusters.
The experiment was implemented on yeast Saccharomyces dataset. Index
Terms-Tri-clustering, Genetic Algorithm, Mean squared residue, Volume, Weights,
Least square approximation.
