Abstract
Clustering is a fundamental task in data analysis: grouping similar objects together into distinguishable subsets. Here, we introduce HDCluster, a novel high-degree graph-based clustering algorithm designed to effectively and rapidly handle various real-world clustering applications, particularly in the context of super-resolution single molecule localization microscopy (SMLM). HDCluster efficiently handles datasets with large and variable numbers of clusters, without requiring prior knowledge of the cluster count, relying on only one parameter. The high speed and efficiency of HDCluster allow it to handle large SMLM datasets with millions of localizations. A comprehensive quantitative comparison against state-of-the-art clustering methods using simulated, public, and real-world datasets demonstrates that HDCluster outperforms other clustering algorithms in terms of time efficiency and clustering performance measures, such as ARI and AMI. HDCluster is particularly robust to noise, making it a promising and effective tool for various clustering tasks in big-data settings, such as SMLM.