基于Canopy聚类的谱聚类算法

A spectral clustering algorithm based on Canopy clustering

传统的谱聚类算法对初始化敏感,针对这个缺陷,引入Canopy算法对样本进行“粗”聚类得到初始聚类中心点,将结果作为K-Means算法的输入,提出了一种基于Canopy和谱聚类融合的聚类算法(Canopy-SC),减少了传统谱聚类算法选择初始中心点的盲目性,并将其用于人脸图像聚类。与传统的谱聚类算法相比,Canopy-SC算法能够得到较好的聚类中心和聚类结果,同时具有更高的聚类精确度。实验结果表明了该算法的有效性和可行性。

The traditional spectral clustering algorithm is sensitive to initialization. Aiming at this defect, we introduce the canopy algorithm to conduct coarse cluster and get the initial clustering center as the input of the K-Means algorithm. Then we propose a spectral clustering algorithm based on canopy clustering (Canopy-SC) to reduce the blind selection of the initial center of the traditional spectral clustering algorithm. We apply the new algorithm to face image clustering. Compared with the traditional spectral clustering algorithm, the Canopy-SC algorithm can not only get better clustering centers and results, but also has a higher clustering accuracy. Experiments demonstrate its effectiveness and feasibility.