@article{partitioning89, author = {Pothen, A. and Simon, H.D. and Liou, K.P.}, title = {Partitioning Sparse Matrices with Eigenvectors of Graphs}, journal = {SIAM J. MATRIX ANAL. APPLIC.}, year = {1990}, volume = {11}, number = {3}, pages = {430--452}, url = {http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970011963_1997016998.pdf }, keywords = {clustering, community, graph, partitioning, spectral, theory} }

%0 = article %A = Pothen, A. and Simon, H.D. and Liou, K.P. %D = 1990 %T = Partitioning Sparse Matrices with Eigenvectors of Graphs %U = http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19970011963_1997016998.pdf

@unpublished{ranade:sus, author = {Ranade, A.G.}, title = {Some uses of spectral methods}, year = {2000}, keywords = {clustering, graph, spectral, svd, theory} }

%0 = unpublished %A = Ranade, A.G. %B = } %C = %D = 2000 %I = %T = Some uses of spectral methods} %U =

@techreport{Spielman:1996, author = {Spielman, Daniel A. and Teng, Shang}, title = {Spectral Partitioning Works: Planar Graphs and Finite Element Meshes}, publisher = {University of California at Berkeley}, address = {Berkeley, CA, USA}, year = {1996}, keywords = {clustering, community, detection, graph, spectral, survey, theory} }

%0 = techreport %A = Spielman, Daniel A. and Teng, Shang %B = } %C = Berkeley, CA, USA %D = 1996 %I = University of California at Berkeley %T = Spectral Partitioning Works: Planar Graphs and Finite Element Meshes} %U =

@article{donath1973lbp, author = {Donath, W.E. and Hoffman, A.J.}, title = {Lower bounds for the partitioning of graphs}, journal = {IBM Journal of Research and Development}, year = {1973}, volume = {17}, number = {5}, pages = {420--425}, keywords = {clustering, community, detection, graph, spectral, theory} }

%0 = article %A = Donath, W.E. and Hoffman, A.J. %D = 1973 %T = Lower bounds for the partitioning of graphs

Despite many empirical successes of spectral clustering methods| algorithms that cluster points using eigenvectors of matrices derived from the data|there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly dierent ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems. 1

@inproceedings{Ng01onspectral, author = {Ng, Andrew Y. and Jordan, Michael I. and Weiss, Yair}, title = {On spectral clustering: Analysis and an algorithm}, booktitle = {Advances in Neural Information Processing Systems 14}, publisher = {MIT Press}, year = {2001}, pages = {849--856}, keywords = {clustering, community, detection, graph, spectral, theory}, abstract = {Despite many empirical successes of spectral clustering methods| algorithms that cluster points using eigenvectors of matrices derived from the data|there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly dierent ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems. 1} }

%0 = inproceedings %A = Ng, Andrew Y. and Jordan, Michael I. and Weiss, Yair %B = Advances in Neural Information Processing Systems 14 %D = 2001 %I = MIT Press %T = On spectral clustering: Analysis and an algorithm