一种基于流形距离核的谱聚类算法

A Spectral Clustering Algorithm Based on Manifold Distance Kernel

针对标准谱聚类算法中,基于欧氏距离的相似性度量不能完全反映数据聚类复杂的空间分布特性的问题,提出了一种基于流形距离核的谱聚类算法.它能充分挖掘数据集中的内在结构信息,较好地反映局部和全局一致性,并且可以很好地防止"桥"噪声点的影响,提高算法的聚类性能.与传统的聚类算法和常见谱聚类算法进行了比较,在人工数据集和UCI数据集上的实验都验证了本算法能够获得更好的聚类效果.

For the problem that the similarity measure based on Euclidean distance cannot fully reflect the complex space distribution of data clustering in the standard spectral clustering algorithm,a novel spectral clustering algorithm is proposed based on manifold distance kernel.It can fully exploit the inherent structure information of the datasets.The proposed algorithm not only can reflect local and global consistency better,but also can prevent the influence of ＂bridge＂ noise points,which improves the algorithm’s clustering performance.Experimental results show that compared with traditional clustering algorithms and those popular spectral clustering algorithms,the algorithm can achieve better clustering effect on artificial datasets and UCI public datasets.