On spectral clustering: Analysis and an algorithm Export

@misc{citeulike:1158897, 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.}, author = {Ng, A. and Jordan, M. and Weiss, Y.}, citeulike-article-id = {1158897}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100}, keywords = {clustering, linear-algebra, machine-learning}, posted-at = {2009-11-07 21:03:11}, priority = {2}, title = {On spectral clustering: Analysis and an algorithm}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100}, year = {2001} }

TY - GEN ID - citeulike:1158897 L3 - citeulike-article-id:1158897 N2 - 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. TI - On spectral clustering: Analysis and an algorithm KW - clustering KW - linear-algebra KW - machine-learning AU - Ng, A AU - Jordan, M AU - Weiss, Y PY - 2001/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.19.8100 ER -