| Title: |
FLASC: a flare-sensitive clustering algorithm |
| Authors: |
BOT, Daniël M.; PEETERS, Jannes; LIESENBORGS, Jori; AERTS, Jan |
| Contributors: |
BOT, Daniël M.; PEETERS, Jannes; LIESENBORGS, Jori; AERTS, Jan |
| Publication Year: |
2025 |
| Collection: |
Document Server@UHasselt (Universiteit Hasselt) |
| Subject Terms: |
Subjects Algorithms and Analysis of Algorithms; Data Mining and Machine Learning; Data Science Keywords Exploratory data analysis; Density-based clustering; Branch-hierarchy detection; HDBSCAN |
| Description: |
Exploratory data analysis workflows often use clustering algorithms to find groups of similar data points. The shape of these clusters can provide meaningful information about the data. For example, a Y-shaped cluster might represent an evolving process with two distinct outcomes. This article presents flare-sensitive clustering (FLASC), an algorithm that detects branches within clusters to identify such shape-based subgroups. FLASC builds upon HDBSCAN*---a state-of-the-art density-based clustering algorithm---and detects branches in a post-processing step using within-cluster connectivity. Two algorithm variants are presented, which trade computational cost for noise robustness. We show that both variants scale similarly to HDBSCAN* regarding computational cost and provide similar outputs across repeated runs. In addition, we demonstrate the benefit of branch detection on two real-world data sets. Our implementation is included in the hdbscan Python package and available as a standalone package at https://github.com/vda-lab/pyflasc. ; Funding This work was supported by KU Leuven grant STG/23/040 and Hasselt University BOF grants (BOF20OWB33) and (BOF21DOC19). The funders had no role in study design,, data collection and analysis, decision to publish, or preparation of the manuscript. ACKNOWLEDGEMENTS We thank Kris Luyten for his comments on an early version of the manuscript. Grammarly was used in the preparation of this manuscript. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
PeerJ Computer Science, 11; https://hdl.handle.net/1942/45980; e2792; 11; 001480533400001 |
| DOI: |
10.7717/peerj-cs.2792 |
| Availability: |
https://hdl.handle.net/1942/45980; https://doi.org/10.7717/peerj-cs.2792 |
| Rights: |
Copyright 2025 Bot et al. Distributed under Creative Commons CC-BY 4.0 ; info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.4A22DBC |
| Database: |
BASE |