关键词: 谱聚类, 基于路径的谱聚类, 基于密度的谱聚类

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.

Key words: spectral clustering, path-based spectral clustering, density-based spectral clustering