Spectral clustering has become increasingly popular in recent years.Being a pairwise method

the success of spectral clustering depends heavily on the choice of similarity measure.Through analyzing the property of data clusters

a novel data-dependent similarity measure is proposed

namely density-sensitive similarity measure

which has the ability of describing the characters of data clustering compared with the traditional Euclidian metric based similarity measure.Based on the novel similarity measure

we have a density-sensitive spectral clustering algorithm.Compared with the original spectral clustering

it has the advantages of effectively dealing with the multi-scale problems and relatively not sensitive to parameter.It obtains promising results not only on artificial datasets but also on USPS handwritten digit dataset.