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We present \textsc{AstroLink}, an efficient and versatile clusteringalgorithm designed to hierarchically classify astrophysically-relevantstructures from both synthetic and observational data sets. We build upon\textsc{CluSTAR-ND}, a hierarchical galaxy/(sub)halo finder, so that\textsc{AstroLink} now generates a two-dimensional representation of theimplicit clustering structure as well as ensuring that clusters arestatistically distinct from the noisy density fluctuations implicit within the$n$-dimensional input data. This redesign replaces the three cluster extractionparameters from \textsc{CluSTAR-ND} with a single parameter, $S$ -- the lowerstatistical significance threshold of clusters, which can be automatically andreliably estimated via a dynamical model-fitting process. We demonstrate therobustness of this approach compared to \textsc{AstroLink}'s predecessors byapplying each algorithm to a suite of simulated galaxies defined over variousfeature spaces. We find that \textsc{AstroLink} delivers a more powerfulclustering performance without suffering from computational drawbacks. Withthese improvements, \textsc{AstroLink} is ideally suited to extracting ameaningful set of hierarchical and arbitrarily-shaped astrophysical clustersfrom both synthetic and observational data sets -- lending itself as a greattool for morphological decomposition within the context of hierarchicalstructure formation.

The Hierarchical Structure of Galactic Haloes: Differentiating Clusters from Stochastic Clumping with \textsc{AstroLink}