Spectral clustering: Difference between revisions

Revision as of 13:43, 28 June 2012

Revision as of 13:46, 28 June 2012

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Transforming the spectral clustering problem into a weighted kernel ''k''-means problem greatly reduces the computational burden.<ref>{{cite journal|last=Dhillon|first=Inderjit|coauthors=Yuqiang Guan, Brian Kulis|title=Weighted Graph Cuts without Eigenvectors: A Multilevel Approach|journal=IEEE Transactions on Pattern Analysis and Machine Intelligence|date=November 2007|year=2007|volume=29|issue=11|pages=1–14|accessdate=25 February 2012}}</ref>

== References ==

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== See also ==

* [[Kernel principal component analysis]]

* [[Cluster analysis]]

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== References ==

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[[Category:Data clustering algorithms]]