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 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.