An SVD based Real Coded Genetic Algorithm for Graph Clustering

Parthajit Roy, Jyotsna Kumar Mandal

Abstract


This paper proposes a novel graph clustering model based on genetic algorithm using a random point bipartite graph. The model uses random points distributed uniformly in the data space and the measurement of distance from these points to the test points have been considered as proximity. Random points and test points create an adjacency matrix. To create a similarity matrix, correlation coefficients are computed from the given bipartite graph. The eigenvectors of the singular value decomposition of the weighted similarity matrix are considered and the same are passed to an elitist GA model for identifying the cluster centers. The model has been tasted with the standard datasets and the performance has been compared with existing standard algorithms.

Keywords


Bipartite Graph, Cluster Analysis, Cluster Validity Index, Genetic Algorithm, Graph Clustering, Singular Value Decomposition

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DOI: http://doi.org/10.11591/ijai.v5.i2.pp64-71
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.