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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Vydáno v:	European journal of operational research Ročník 211; číslo 2; s. 221 - 231
Hlavní autoři:	Nascimento, Mariá C.V., de Carvalho, André C.P.L.F.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.06.2011 Elsevier Elsevier Sequoia S.A
Edice:	European Journal of Operational Research
Témata:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Cluster analysis Clustering Computer science; control theory; systems Cutting stock problem Data processing. List processing. Character string processing Exact sciences and technology Graph algorithms Graphs Information retrieval. Graph Laplace transforms Mathematical analysis Matrix Memory organisation. Data processing Min-cut Modularity ncut Operational research Partitioning Ratio cut Software Spectra Spectral clustering Spectral clustering Min-cut Ratio cut ncut Modularity Spectral methods Studies Theoretical computing 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
ISSN:	0377-2217, 1872-6860
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Popis
Shrnutí:	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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0377-2217 1872-6860
DOI:	10.1016/j.ejor.2010.08.012