A Heuristic for the Automatic Parametrization of the Spectral Clustering Algorithm
Pierrick Bruneau, Olivier Parisot, Benoît Otjacques · 2014
Finding the optimal number of groups in the context of a clustering algorithm is identified as a difficult problem. In this article, we automate this choice for the spectral clustering algorithm with a novel heuristic. Our method is deterministic, and remarkable by its low computational burden. We show its effectiveness with respect to the state of the art, and further investigate assumptions underlying previous work through an empirical study, with the support of synthetic and real data sets.