Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 211, Issue 2, Pages 221-231Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2010.08.012
Keywords
Spectral clustering; Min-cut; Ratio cut; ncut; Modularity
Funding
- CAPES
- CNPq
- FAPESP
Ask authors/readers for more resources
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. (C) 2010 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available