扩展约束的半监督谱聚类算法研究

Research of constraints-expansion semi-supervised spectral clustering algorithm

通过对几种典型聚类算法的分析和比较,提出了一种新的聚类算法,基于扩展约束的半监督谱聚类算法,简称CE-SSC。这种算法扩展了已知约束集,通过密度敏感距离改变样本点的相似关系,结合半监督谱聚类进行聚类。在UCI基准集上的仿真实验结果证明,基于扩展约束的半监督谱聚类算法具有良好的聚类效应。

Based on several typical clustering algorithm analysis and comparison, this paper proposes a new clustering based on constraint expansion（CESSC）. This algorithm expands the known constraints set, changes the similarity relation of the sample points through the density-sensitive path distance, and then combines with semi-supervised spectral clustering to cluster. Experimental results on UCI benchmark data sets prove that CESSC algorithm has good clustering effect.