Spectral clustering is a powerful technique in data analysis that has found
increasing support and application in many areas. This report is geared to
give an introduction to its methods, presenting the most common algorithms,
discussing advantages and disadvantages of each, rather than endorsing one
of them as the best, because, arguably, there is no black-box algorithm,
which performs equally well for any data. We present results from previous
studies and conclude that methods based on Ncut and multiway are most
promising for general application.
%0 Report
%1 Auffarth2007Spectral
%A Auffarth, Benjamin
%D 2007
%K clustering entityguides graph
%T Spectral Graph Clustering
%U http://www-lehre.inf.uos.de/\~bauffart/spectral.pdf
%X Spectral clustering is a powerful technique in data analysis that has found
increasing support and application in many areas. This report is geared to
give an introduction to its methods, presenting the most common algorithms,
discussing advantages and disadvantages of each, rather than endorsing one
of them as the best, because, arguably, there is no black-box algorithm,
which performs equally well for any data. We present results from previous
studies and conclude that methods based on Ncut and multiway are most
promising for general application.
@techreport{Auffarth2007Spectral,
abstract = {Spectral clustering is a powerful technique in data analysis that has found
increasing support and application in many areas. This report is geared to
give an introduction to its methods, presenting the most common algorithms,
discussing advantages and disadvantages of each, rather than endorsing one
of them as the best, because, arguably, there is no black-box algorithm,
which performs equally well for any data. We present results from previous
studies and conclude that methods based on Ncut and multiway are most
promising for general application.},
added-at = {2009-03-12T15:42:50.000+0100},
author = {Auffarth, Benjamin},
biburl = {https://www.bibsonomy.org/bibtex/2ccd604d036e63c0660122feb67406e63/lillejul},
citeulike-article-id = {4043067},
interhash = {b2dcc6aea4839729b7896d708972d57d},
intrahash = {ccd604d036e63c0660122feb67406e63},
keywords = {clustering entityguides graph},
month = {January},
posted-at = {2009-02-13 10:34:55},
priority = {2},
school = {Universitat Polit\`{e}cnica de Catalunya},
timestamp = {2009-03-12T15:42:50.000+0100},
title = {Spectral Graph Clustering},
url = {http://www-lehre.inf.uos.de/\~{}bauffart/spectral.pdf},
year = 2007
}