Article
Real Coded Genetic Algorithm for Graph Clustering Based on SVD
This work uses a random point bipartite graph to present a new genetic algorithm-based graph clustering model. The model makes use of uniformly distributed random points in the data space, and gap between these points and the test points is measured and regarded as closeness. An adjacency matrix is produced using test points and random points. Correlation coefficients are calculated using the provided bipartite graph to produce a similarity matrix. To find the cluster centers, the eigenvectors of the weighted similarity matrix's singular value decomposition are taken into account and fed into an exclusive GA model. The model's performance has been compared to current standard algorithms, and it has been tested using standard datasets.
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