Title :
Review on spectral methods for clustering
Author :
JingMao, Zhang ; YanXia, Shen
Author_Institution :
College of Internet of Things engineering, Jiangnan University, Wuxi 214122
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;
Conference_Titel :
Control Conference (CCC), 2015 34th Chinese
Conference_Location :
Hangzhou, China
DOI :
10.1109/ChiCC.2015.7260226
