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FLASC: a flare-sensitive clustering algorithm
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DOI：10.7717/peerj-cs.2792.png)
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Abstract
En 中文
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.
Keywords:
Exploratory data analysis
Density-based clustering
Branch-hierarchy detection
HDBSCAN*
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