FLASC: A Flare-Sensitive Clustering Algorithm: Extending HDBSCAN* for
  Detecting Branches in Clusters
D. M. Bot J. Peeters J. Liesenborgs J. Aerts
We present FLASC, an algorithm for flare-sensitive clustering. Our algorithm
builds upon HDBSCAN* -- which provides high-quality density-based clustering
performance -- through a post-processing step that differentiates branches
within the detected clusters' manifold, adding a type of pattern that can be
discovered. Two variants of the algorithm are presented, which trade
computational cost for noise robustness. We show that both variants scale
similarly to HDBSCAN* in terms of computational cost and provide stable outputs
using synthetic data sets, resulting in an efficient flare-sensitive clustering
algorithm. In addition, we demonstrate the algorithm's benefit in data
exploration over HDBSCAN* clustering on two real-world data sets.