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Community Detection in Multidimensional Genomic Dataset

N. S. Sonumol, V. R. Uma, Sreeja Ashok, M. V. Judy


Community detection helps us to group the similarly behaving objects that are coupled compactly. The communities have concrete relationships internally where as thin connections between groups. It finds its application in real life scenarios where the data can be represented in a graphical format.The efficiency of community identification algorithm is abridged with the increase in high dimensions and huge variance of the dataset. We integrate Preprocessing steps like Normalization and Dimensionality reduction methods to evaluate the performance of existing community detection algorithms for high dimensional data set using modularity measures. The performance of the proposed framework is compared using benchmark datasets in different community detection algorithm


Community Detection, Feature selection, Normalization, Modularity, Eigen vector Algorithm, Fast greedy Algorithm, Multilevel Algorithm, Walk trap Algorithm

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