Annealing and the normalized N-cut

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Annealing and the normalized N-cut
We describe an annealing procedure that computes the normalized N-cut of a weighted graph G. The first phase transition computes the solution of the approximate normalized 2-cut problem, while the low temperature solution computes the normalized N-cut. The intermediate solutions provide a sequence of refinements of the 2-cut that can be used to split the data to K clusters with 2 K N. This approach only requires specification of the upper limit on the number of expected clusters N, since by controlling the annealing parameter we can obtain any number of clusters K with 2 K N. We test the algorithm on an image segmentation problem and apply it to a problem of clustering high dimensional data from the sensory system of a cricket. Key words: Clustering, annealing, normalized N-cut.
Tomás Gedeon, Albert E. Parker, Collette Ca
Added 28 Dec 2010
Updated 28 Dec 2010
Type Journal
Year 2008
Where PR
Authors Tomás Gedeon, Albert E. Parker, Collette Campion, Zane Aldworth
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