A. Ng, M. Jordan, and Y. Weiss. Advances in Neural Information Processing Systems 14, page 849--856. MIT Press, (2001)
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
Description
CiteSeerX — On spectral clustering: Analysis and an algorithm
%0 Conference Paper
%1 Ng01onspectral
%A Ng, Andrew Y.
%A Jordan, Michael I.
%A Weiss, Yair
%B Advances in Neural Information Processing Systems 14
%D 2001
%I MIT Press
%K clustering community detection graph spectral theory
%P 849--856
%T On spectral clustering: Analysis and an algorithm
%X 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,
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},
added-at = {2009-04-01T07:05:02.000+0200},
author = {Ng, Andrew Y. and Jordan, Michael I. and Weiss, Yair},
biburl = {https://www.bibsonomy.org/bibtex/27485849e42418ee5ceefb45dc6eb603c/folke},
booktitle = {Advances in Neural Information Processing Systems 14},
description = {CiteSeerX — On spectral clustering: Analysis and an algorithm},
interhash = {b72c97e659127fc653a0d51143d85b0c},
intrahash = {7485849e42418ee5ceefb45dc6eb603c},
keywords = {clustering community detection graph spectral theory},
pages = {849--856},
publisher = {MIT Press},
timestamp = {2009-04-01T07:05:02.000+0200},
title = {On spectral clustering: Analysis and an algorithm},
year = 2001
}