基于密度自适应邻域相似图的半监督谱聚类.

The spectral clustering is a clustering algorithm based on the theory of spectral partitioning. The traditional spectral clustering algorithm belongs to unsupervised learning algorithms and can only utilize a single type of data to cluster. Based on the situation, this paper proposed a semi-supervised spectral clustering algorithm based on density adaptive neighbor similarity graph( DAN-SSC). DAN-SSC algorithm combined the idea of semi-supervised learning on the basis of the traditional spectral clustering algorithm, it solved the problem that the traditional spectral clustering algorithm couldn ' t fully utilize all the data and had to abandon some labeled data. It also spread a small amount of pairwise constrained prior information to the entire space and made it guide the process of clustering better. The results of experiments show that the proposed algorithm is feasible and effective. [ABSTRACT FROM AUTHOR]