HEADSS (HiErArchical Data Splitting and Stitching) facilitates clustering at scale, unlike clustering algorithms that scale poorly with increased data volume or that are intrinsically non-distributed. HEADSS automates data splitting and stitching, allowing repeatable handling, and removal, of edge effects. Implemented in conjunction with scikit's HDBSCAN, the code achieves orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. HEADSS also establishes a hierarchy of features by using a subset of clustering features to split the data.

WaldoInSky finds anomalous astronomical light curves and their analogs. The package contains four methods: an adaptation of the Unsupervised Random Forest for anomaly detection in light curves that operates on the light curve points and their power spectra; two manifold-learning methods (the t-SNE and UMAP) that operate on the DMDT maps (image representations of the light curves), and that can be used to find analog light curves in the low-dimensional representation; and an Isolation Forest method for evaluating approaches of light curve pre-processing, before they are passed to the anomaly detectors. WaldoInSky also contain code for random sparsification of light curves.