Review on spectral methods for clustering

Spectral clustering(SC) is a clustering technology based on graph theory. It becomes one of the most hot topics on clustering because that it can get global optimal solution without any assumptions on data's structure. In this paper, the basic graph theories including some typical graph cut met...

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Bibliographic Details
Main Authors: JingMao, Zhang, YanXia, Shen
Format: Conference Proceeding
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Classification algorithms
Clustering
Clustering algorithms
Conferences
Data structures
graph cut
Graph theory
Graphs
Image segmentation
Laplace equations
Laplacian matrix
Optimization
Partitioning algorithms
similarity matrix
Spectra
Spectral clustering
Spectral methods
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Description
Summary:Spectral clustering(SC) is a clustering technology based on graph theory. It becomes one of the most hot topics on clustering because that it can get global optimal solution without any assumptions on data's structure. In this paper, the basic graph theories including some typical graph cut methods for SC are described, then, classic SC algorithms are introduced. Several problems and research topics on SC are also predicted at the end of this paper.
ISSN:2161-2927
1934-1768
DOI:10.1109/ChiCC.2015.7260226