NeurIPS 2016oral106 citations
Hierarchical Clustering via Spreading Metrics
Abstract
We study the cost function for hierarchical clusterings introduced by [Dasgupta, 2015] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [Dasgupta, 2015] that a top-down algorithm returns a hierarchical clustering of cost at most (O\left(\alpha
BibTeX
@inproceedings{NIPS2016_4d2e7bd3,
author = {Roy, Aurko and Pokutta, Sebastian},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Hierarchical Clustering via Spreading Metrics},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/4d2e7bd33c475784381a64e43e50922f-Paper.pdf},
volume = {29},
year = {2016}
}