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.

• Type: Journal
• Year: 2008
• Where: PR
• Authors: Tomás Gedeon, Albert E. Parker, Collette Campion, Zane Aldworth
• Keywords: Normalized 2-cut Problem, PR 2006, PR 2008, Solutions, Weighted Graph G