This variant of spectral clustering (Shi and Malik, 2000) modifies the base algorithm by replacing the step that finds eigenvectors of the unnormalized Laplacian alone. Instead, it solves the generalized eigenproblem $Lv = \lambda Dv$, where $L$ is the unnormalized Laplacian and $D$ is the degree matrix. The resulting eigenvectors are then used for clustering in place of the unnormalized Laplacian's eigenvectors.