We study density-based clustering under low-noise conditions. Our
framework allows for sharply defined clusters such as clusters on
lower dimensional manifolds. 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 known
as the ``friends-of-friends'' algorithm successfully approximates
the high density clusters.