We present a generic dynamic programming method to compute the optimal
clustering of n scalar elements into k pairwise disjoint intervals. This
case includes 1D Euclidean k-means, k-medoids, k-medians, k-centers,
etc. We extend the method to incorporate cluster size constraints and show how
to choose the appropriate k 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