Spectral methods for graph clustering – A survey
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...
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| Published in: | European journal of operational research Vol. 211; no. 2; pp. 221 - 231 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Amsterdam
Elsevier B.V
01.06.2011
Elsevier Elsevier Sequoia S.A |
| Series: | European Journal of Operational Research |
| Subjects: | |
| ISSN: | 0377-2217, 1872-6860 |
| Online Access: | Get full text |
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| 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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| AbstractList | 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. [PUBLICATION 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. |
| Author | Nascimento, Mariá C.V. de Carvalho, André C.P.L.F. |
| Author_xml | – sequence: 1 givenname: Mariá C.V. surname: Nascimento fullname: Nascimento, Mariá C.V. email: mariah@icmc.usp.br – sequence: 2 givenname: André C.P.L.F. surname: de Carvalho fullname: de Carvalho, André C.P.L.F. email: andre@icmc.usp.br |
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| Keywords | Spectral clustering Ratio cut Modularity Min-cut ncut Cluster analysis Spectral method Modular system Graph theory Data mining Weighted graph Laplacian Graph decomposition Graph cut Classification Graph method |
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| Title | Spectral methods for graph clustering – A survey |
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