Nonparametric Nearest Neighbor Descent Clustering based on Delaunay Triangulation
This work addresses clustering challenges for data analysis, but it appears incremental as it builds on previous parametric in-tree methods.
The authors tackled the problem of clustering by proposing a nonparametric method to compute potential values using Delaunay Triangulation, which generates many local extremes but is handled effectively by their in-tree-based approach, demonstrating its superiority over gradient-based methods.
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.