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.
"Generalized density clustering." Ann. Statist. 38 (5) 2678 - 2722, October 2010. https://doi.org/10.1214/10-AOS797