BibTeX
@TECHREPORT{Xu03multiwaycuts,
author = {Liang Xu},
title = {Multiway cuts and spectral clustering},
institution = {},
year = {2003}
}
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Abstract
Abstract We look at spectral clustering as optimization. We show that near some special points called perfect, spectral clustering optimizes simultaneously two criteria: a dissimilarity measure that we call the multiway normalized cut (MNCut) and a cluster coherence measure that we call the gap. The immediate implication from the user's p.o.v is that spectral clustering will optimize any tradeoff between MNCut and gap which may explain its success in practice. Finally, we propose new methods for selecting K based on the gap and show their superior performance in experiments.
