Spectral clustering is a powerful clustering method for document data set. However, spectral clustering needs to solve an eigenvalue problem of the matrix converted from the simil...
Spectral clustering algorithms have been shown to be more effective in finding clusters than some traditional algorithms such as k-means. However, spectral clustering suffers fro...
Spectral clustering refers to a class of techniques which rely on the eigenstructure of a similarity matrix to partition points into disjoint clusters, with points in the same clu...
Given a set of n randomly drawn sample points, spectral clustering in its simplest form uses the second eigenvector of the graph Laplacian matrix, constructed on the similarity gra...
Ulrike von Luxburg, Olivier Bousquet, Mikhail Belk...
Spectral clustering is a simple yet powerful method for finding structure in data using spectral properties of an associated pairwise similarity matrix. This paper provides new in...