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.
Publié le : 2010-10-15
Classification:
Density clustering,
kernel density estimation,
62H30,
62G07
@article{1278861457,
author = {Rinaldo, Alessandro and Wasserman, Larry},
title = {Generalized density clustering},
journal = {Ann. Statist.},
volume = {38},
number = {1},
year = {2010},
pages = { 2678-2722},
language = {en},
url = {http://dml.mathdoc.fr/item/1278861457}
}
Rinaldo, Alessandro; Wasserman, Larry. Generalized density clustering. Ann. Statist., Tome 38 (2010) no. 1, pp. 2678-2722. http://gdmltest.u-ga.fr/item/1278861457/