  Most density-based clustering methods largely rely on how well the underlying
density is estimated. However, density estimation itself is also a challenging
problem, especially the determination of the kernel bandwidth. A large
bandwidth could lead to the over-smoothed density estimation in which the
number of density peaks could be less than the true clusters, while a small
bandwidth could lead to the under-smoothed density estimation in which spurious
density peaks, or called the "ripple noise", would be generated in the
estimated density. In this paper, we propose a density-based hierarchical
clustering method, called the Deep Nearest Neighbor Descent (D-NND), which
could learn the underlying density structure layer by layer and capture the
cluster structure at the same time. The over-smoothed density estimation could
be largely avoided and the negative effect of the under-estimated cases could
be also largely reduced. Overall, D-NND presents not only the strong capability
of discovering the underlying cluster structure but also the remarkable
reliability due to its insensitivity to parameters.
