Rates of Convergence for the Cluster Tree
Abstract
For a density f on R^d, a high-density cluster is any connected component of x: f(x) >= c, for some c > 0. The set of all high-density clusters form a hierarchy called the cluster tree of f. We present a procedure for estimating the cluster tree given samples from f. We give finite-sample convergence rates for our algorithm, as well as lower bounds on the sample complexity of this estimation problem.
Cite
Text
Chaudhuri and Dasgupta. "Rates of Convergence for the Cluster Tree." Neural Information Processing Systems, 2010.Markdown
[Chaudhuri and Dasgupta. "Rates of Convergence for the Cluster Tree." Neural Information Processing Systems, 2010.](https://mlanthology.org/neurips/2010/chaudhuri2010neurips-rates/)BibTeX
@inproceedings{chaudhuri2010neurips-rates,
title = {{Rates of Convergence for the Cluster Tree}},
author = {Chaudhuri, Kamalika and Dasgupta, Sanjoy},
booktitle = {Neural Information Processing Systems},
year = {2010},
pages = {343-351},
url = {https://mlanthology.org/neurips/2010/chaudhuri2010neurips-rates/}
}