Spectral methods for graph clustering - A survey |
| |
Authors: | Mariá C.V. Nascimento,André C.P.L.F. de Carvalho |
| |
Affiliation: | Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, Caixa Postal 668, São Carlos-SP, CEP 13560-970, Brazil |
| |
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. |
| |
Keywords: | Spectral clustering Min-cut Ratio cut ncut Modularity |
本文献已被 ScienceDirect 等数据库收录! |
|