Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
478504 | European Journal of Operational Research | 2011 | 11 Pages |
Abstract
Graph clustering is an area in cluster analysis that looks for groups of related vertices in a graph. Due to its large applicability, several graph clustering algorithms have been proposed in the last years. A particular class of graph clustering algorithms is known as spectral clustering algorithms. These algorithms are mostly based on the eigen-decomposition of Laplacian matrices of either weighted or unweighted graphs. This survey presents different graph clustering formulations, most of which based on graph cut and partitioning problems, and describes the main spectral clustering algorithms found in literature that solve these problems.
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Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Mariá C.V. Nascimento, André C.P.L.F. de Carvalho,