59 research outputs found
Optimal interval clustering: Application to Bregman clustering and statistical mixture learning
We present a generic dynamic programming method to compute the optimal
clustering of scalar elements into pairwise disjoint intervals. This
case includes 1D Euclidean -means, -medoids, -medians, -centers,
etc. We extend the method to incorporate cluster size constraints and show how
to choose the appropriate by model selection. Finally, we illustrate and
refine the method on two case studies: Bregman clustering and statistical
mixture learning maximizing the complete likelihood.Comment: 10 pages, 3 figure
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