Generalized density clustering
Statistics Theory
2010-11-11 v3 Statistics Theory
Abstract
We study generalized density-based clustering in which sharply defined clusters such as clusters on lower-dimensional manifolds are allowed. We show that accurate clustering is possible even in high dimensions. We propose two data-based methods for choosing the bandwidth and we study the stability properties of density clusters. We show that a simple graph-based algorithm successfully approximates the high density clusters.
Keywords
Cite
@article{arxiv.0907.3454,
title = {Generalized density clustering},
author = {Alessandro Rinaldo and Larry Wasserman},
journal= {arXiv preprint arXiv:0907.3454},
year = {2010}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOS797 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)