Spectral clustering based on neighboring adaptive local scale
近邻自适应局部尺度的谱聚类算法

Considering the performance of traditional spectral clustering using Gaussian kernels,a new spectral clustering based on neighboring adaptive local scale is presented in this paper.Based on clustering consistency characteristics,the proposed method first emphasizes the flexibility of the local scale,which means each sample has a corresponding scale parameter.Furthermore,it overcomes the limitations of traditional methods in all samples with the same global scale parameter.Hence,it can depict the intrinsic structure of data sets better.Second,it stresses the convenience of parameter selection.It can determine the value of a local scale for one sample by computing the sum of weighted distances of N neighbors.Therefore,it can determine the scale parameter automatically.This paper illustrates the proposed algorithm not only has inhibition for certain outliers but is able to cluster the data sets with different scales.Finally,experiments on both,artificial data and UCI data sets,show that the proposed method is effective.