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Line 53: \| booktitle = Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining \| pages = 551–556 ~~\| organization = ACM~~ }}</ref> 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> == See also ==

Spectral clustering: Difference between revisions