Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is ``relaxed'' into a tractable eigenvector problem, and in which the relaxed solution is subsequently ``rounded'' into an approximate discrete solution to the original problem. In this paper we present a novel margin-based perspective on multiway spectral clustering. We show that the margin-based perspective illuminates both the relaxation and rounding aspects of spectral clustering, providing a unified analysis of existing algorithms and guiding the design of new algorithms. We also present connections between spectral clustering and several other topics in statistics, specifically minimum-variance clustering, Procrustes analysis and Gaussian intrinsic autoregression.