How to decide a proper scale parameter is still an issue to deal with. In this paper, we propose an adaptive spectral clustering algorithm based on weighted density
(WDSC), which solves the sensitivity of the scale parameter in the similarity matrix made from Gaussian kernels. It takes weighted K nearest neighbor distance of each data as the scale parameter, and the reciprocal of the scale parameter as its density. It also brings in a new density contrast to adjust the similarity matrix. It takes the neighbor distribution of each data into consideration, so it is robust to outliers and insensitive to scale parameters. Experiments on different datasets and comparative experiments demonstrate the effectiveness and robustness of the proposed algorithm.