@article{MAKHILLJEAS2017121114523,
    title = {Edge Pruning and GA-Based Clustering Approach for Biological Data Analysis},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {11},
    pages = {2990-2995},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.2990.2995},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.2990.2995},
    author = {Athira A. and},
    keywords = {Edge pruning,Genetic algorithm,mutation,centrality,clusters},
    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.}
    }