%0 Journal Article %1 nielsen2014optimal %A Nielsen, Frank %A Nock, Richard %D 2014 %K clustering divergences %T Optimal interval clustering: Application to Bregman clustering and statistical mixture learning %U http://arxiv.org/abs/1403.2485 %X 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.

@article{nielsen2014optimal, abstract = {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.}, added-at = {2019-12-11T14:39:04.000+0100}, author = {Nielsen, Frank and Nock, Richard}, biburl = {https://www.bibsonomy.org/bibtex/2aa98a251281c93c4e4851ee3841652cb/kirk86}, description = {[1403.2485] Optimal interval clustering: Application to Bregman clustering and statistical mixture learning}, interhash = {851df3195bbeb53c0f4c7d406e9fb1ee}, intrahash = {aa98a251281c93c4e4851ee3841652cb}, keywords = {clustering divergences}, note = {cite arxiv:1403.2485Comment: 10 pages, 3 figures}, timestamp = {2019-12-11T14:39:04.000+0100}, title = {Optimal interval clustering: Application to Bregman clustering and statistical mixture learning}, url = {http://arxiv.org/abs/1403.2485}, year = 2014 }