DocumentCode
2253257
Title
Review on spectral methods for clustering
Author
JingMao, Zhang ; YanXia, Shen
Author_Institution
College of Internet of Things engineering, Jiangnan University, Wuxi 214122
fYear
2015
fDate
28-30 July 2015
Firstpage
3791
Lastpage
3796
Abstract
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.
Keywords
Algorithm design and analysis; Classification algorithms; Clustering algorithms; Graph theory; Image segmentation; Laplace equations; Partitioning algorithms; Laplacian matrix; Spectral clustering; graph cut; graph theory; similarity matrix;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (CCC), 2015 34th Chinese
Conference_Location
Hangzhou, China
Type
conf
DOI
10.1109/ChiCC.2015.7260226
Filename
7260226
Link To Document