A Survey on Genetic Algorithm Based Clustering Techniques For Micro Array Gene Data

Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. In this paper the nature of microarray data is discussed briefly and a survey on genetic algorithm based clustering techniques for micro array gene data is presented. Some preliminary concepts that form the basis for the development of clustering algorithms are introduced. Finally, some of the most popular clustering techniques like GenClust, HGACLUS, hybrid method using EM algorithm, multiobjective genetic clustering algorithm are discussed. As such, the study provides a framework for the evaluation of clustering in gene expression analysis.