A Framework for Parallelizing Hierarchical Clustering Methods

Published in European Conference on Machine Learning (ECML-PKDD), 2019

This paper is about parallel/distributed implementations of Hierarchical Clustering methods such as Divisive k-Means and Centroid Linkage. In order to get around inherent sequential dependencies in the standard implementations of these methods, we consider relaxed versions which allow for approximate split/merge decisions.