  In this paper, we present a novel non-parametric clustering technique. Our
technique is based on the notion that each latent cluster is comprised of
layers that surround its core, where the external layers, or border points,
implicitly separate the clusters. Unlike previous techniques, such as DBSCAN,
where the cores of the clusters are defined directly by their densities, here
the latent cores are revealed by a progressive peeling of the border points.
Analyzing the density of the local neighborhoods allows identifying the border
points and associating them with points of inner layers. We show that the
peeling process adapts to the local densities and characteristics to
successfully separate adjacent clusters (of possibly different densities). We
extensively tested our technique on large sets of labeled data, including
high-dimensional datasets of deep features that were trained by a convolutional
neural network. We show that our technique is competitive to other
state-of-the-art non-parametric methods using a fixed set of parameters
throughout the experiments.
