Gravity Inspired Clustering Algorithm
Imran Mohammed, Iain B. Collings, Stephen Vaughan Hanly · 2020
This paper presents a new clustering algorithm inspired by Newtonian gravity that iteratively groups data and eliminates outliers. In particular, we impose a grid over the region of interest and define a particle with data-dependent mass for each grid square. We then calculate a Newtonian inspired force on each of the particles and move them in the direction of the force. We repeat the process until there is no further movement. We compare performance with existing algorithms and show that in cases of medium to high clutter, our algorithm has an order of magnitude lower estimation error.