A New Biclustering Method for Gene Expersion Data Based on Adaptive Multi Objective Particle Swarm Optimization

In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model is very suitable for solving this problem. Our method proposes a Hybrid algorithm which is based on adaptive multi objective particle swarm optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping among biclusters and as possible, will cover all elements of gene expression matrix. Experimental result in bench mark data base present a significant improvement in overlap among biclusters and coverage of elements in gene expression and quality of biclusters.