Spectral clustering has become in recent years one of the most popular clustering algorithm. In this talk I discuss a generalized version of spectral clustering based on the second eigenvector of the graph p-Laplacian, a non-linear generalization of the graph Laplacian. The clustering obtained for 1<=2 can be seen as an interpolation of the relaxation of the normalized cut (p=2) and the Cheeger cut (p=1). However, the main motivation for p-spectral clustering is the fact, that one can show that the cut value obtained by thresholding the second eigenvector of the p-Laplacian converges towards the optimal Cheeger cut as p tends to 1. I will also present an efficient implementation which allows to do p-spectral clustering for large scale datasets.