A Flexible Adaptive Stable Clustering Algorithm for Archive-Scale Online Mass Spectrometry
For researchers analyzing large-scale online mass spectrometry data, FASC provides a scalable, stable clustering method that overcomes the trade-off among scalability, metric flexibility, and stability.
FASC is a clustering algorithm that decouples similarity kernel from optimization logic to achieve deterministic, order-independent convergence with linear runtime scaling. It achieved >99.5% cluster purity and 0.99 Adjusted Rand Index on benchmarks, and autonomously mapped atmospheric aging pathways while isolating ultra-rare tracers (<0.2% abundance) from 25 million mass spectra.
Modern online mass spectrometry generates multi-terabyte data streams critical for understanding Earth's environmental systems. However, extracting actionable chemical insights from these repositories is impeded by a computational bottleneck: existing clustering methods force a compromise among scalability, metric flexibility, and algorithmic stability. Here, we introduce Flexible Adaptive Stable Clustering (FASC), a dynamical systems framework that resolves these constraints by architecturally decoupling the similarity kernel from rigorous optimization logic. Unlike legacy heuristics that suffer from stochastic drift and algorithmic blending, FASC employs a Density-Augmented Similarity Selection rule and geometric constraints to guarantee deterministic, order-independent convergence. After validating FASC on canonical machine-learning ground truths (achieving >99.5% cluster purity and 0.99 Adjusted Rand Index), we deployed the framework on 25 million mass spectra of atmospheric aerosols. Demonstrating strictly linear empirical runtime scaling (O(N)), FASC autonomously mapped atmospheric aging pathways of secondary inorganic aerosols while isolating ultra-rare industrial tracers (<0.2% abundance), providing a scalable infrastructure for mining environmental big data.