计算机工程与应用Issue(2)：165-170,6.DOI:10.3778/j.issn.1002-8331.1303-0096
基于密度与路径的稳健谱聚类
Robust density-path-based spectral clustering
许洪玮 1曹江中 1何家峰 1戴青云1
作者信息
- 1. 广东工业大学 信息工程学院，广州 510006
- 折叠
摘要
Abstract
Spectral clustering is wildly studied in the field of class identification in recent years, of which the path-based and density-based algorithms are the two main research topics. These two algorithms have delivered impressive results in some data sets, but are ineffective for some special cases. This paper unites the advantages of these two algorithms and finds the paths under the multi-levels restriction of density, after which a new similarity measure is built. In order to enhance the robustness against noises, robust coefficients are added based on the local information of data set, thus a robust density-path-based spectral clustering method is proposed. Experimental results on synthetic data sets as well as real world data sets demonstrate that the proposed method can obtain more than acceptable results.关键词
谱聚类/基于路径的谱聚类/基于密度的谱聚类Key words
spectral clustering/path-based spectral clustering/density-based spectral clustering分类
信息技术与安全科学引用本文复制引用
许洪玮,曹江中,何家峰,戴青云..基于密度与路径的稳健谱聚类[J].计算机工程与应用,2015,(2):165-170,6.
