  In our physically inspired in-tree (IT) based clustering algorithm and the
series after it, there is only one free parameter involved in computing the
potential value of each point. In this work, based on the Delaunay
Triangulation or its dual Voronoi tessellation, we propose a nonparametric
process to compute potential values by the local information. This computation,
though nonparametric, is relatively very rough, and consequently, many local
extreme points will be generated. However, unlike those gradient-based methods,
our IT-based methods are generally insensitive to those local extremes. This
positively demonstrates the superiority of these parametric (previous) and
nonparametric (in this work) IT-based methods.
