Abstract: Analysis of various kinds of biological data is one of the major problems in bioinformatics. Data mining approaches can be used to uncover hidden patterns and to extract significant knowledge for better analysis and decision making. In this study, we analyse different methods for simplifying the complex networks by identifying significant edges using edge pruning techniques and introduced GA-based clustering process for building optimum subgraphs from the pruned network. The optimum edges were identified by evaluating the similarity between the pair of nodes. Different graph properties like centrality measures are used for positioning the data objects and for improving the cluster cohesiveness. Modularity value was used as the fitness function and mutation operator was performed for deriving the optimum clusters.