期刊
NEURAL COMPUTING & APPLICATIONS
卷 24, 期 7-8, 页码 1477-1486出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-013-1439-2
关键词
Spectral clustering; Graph theory; Graph cut; Laplacian matrix; Eigen-decomposition
资金
- National Key Basic Research Program of China [2013CB329502]
- Fundamental Research Funds for the Central Universities [2013XK10]
Spectral clustering is a clustering method based on algebraic graph theory. It has aroused extensive attention of academia in recent years, due to its solid theoretical foundation, as well as the good performance of clustering. This paper introduces the basic concepts of graph theory and reviews main matrix representations of the graph, then compares the objective functions of typical graph cut methods and explores the nature of spectral clustering algorithm. We also summarize the latest research achievements of spectral clustering and discuss several key issues in spectral clustering, such as how to construct similarity matrix and Laplacian matrix, how to select eigenvectors, how to determine cluster number, and the applications of spectral clustering. At last, we propose several valuable research directions in light of the deficiencies of spectral clustering algorithms.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据