# FLASC: A Flare-Sensitive Clustering Algorithm

arXiv: https://arxiv.org/abs/2311.15887 · HTML: https://mlsanity.com/paper/2311.15887/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 78 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Accelerated Hierarchical Density Clustering](https://mlsanity.com/paper/1705.07321/) (2017)
- [Interactive Visualization of 2-D Persistence Modules](https://mlsanity.com/paper/1512.00180/) (2015)
- [TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Data](https://mlsanity.com/paper/2009.01512/) (2020)
- [Clustering with UMAP: Why and How Connectivity Matters](https://mlsanity.com/paper/2108.05525/) (2021)
- [On the bottleneck stability of rank decompositions of multi-parameter persistence modules](https://mlsanity.com/paper/2208.00300/) (2022)
- [Regularization of Mixture Models for Robust Principal Graph Learning](https://mlsanity.com/paper/2106.09035/) (2021)

## Abstract (arXiv)

Clustering algorithms are often used to find subpopulations in exploratory data analysis workflows. Not only the clusters themselves, but also their shape can represent meaningful subpopulations. In this paper, we present FLASC, an algorithm that detects branches within clusters to identify such subpopulations. FLASC builds upon HDBSCAN*, a state-of-the-art density-based clustering algorithm, and detects branches in a post-processing step that describes within-cluster connectivity. 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 benefit of branch-detection on two real-world data sets.