Glad to see the article mention dropping in the fastcluster package to replace the default R hclust. I would suggest using the parallelDist library as well instead of the standard dist.
Clustering in general and hierarchical clustering have been something I have spent some recent time trying to come up to speed on. Current state of the art seems to be using graph-based approaches of community detection (Louvain method) where graphs are built for a set of samples with N features by starting with a graph of K-nearest-neighbors and assigning some weight to the edges.
Clustering in general and hierarchical clustering have been something I have spent some recent time trying to come up to speed on. Current state of the art seems to be using graph-based approaches of community detection (Louvain method) where graphs are built for a set of samples with N features by starting with a graph of K-nearest-neighbors and assigning some weight to the edges.