Spectral Clustering

This article talks about Spectral Clustering, which is a techniques to make use of the spectrum (eigenvalues) of the similarity matrix of the data to perform dimensionality reduction before clustering in fewer dimensions. The goal of spectral clustering is to cluster data that is connected but not lnecessarily compact or clustered within convex boundaries. It is closely related to Nonlinear dimensionality reduction, and dimension reduction techniques such as locally-linear embedding can be used to reduce errors from noise or outliers