[pull] develop from photoprism:develop - #1772
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DBSCAN takes a single link distance, so a set holding groups of different densities has no value that suits all of them: whatever connects the sparsest group also chains the closest pair together. OPTICS orders points by reachability and extracts clusters from that ordering, either at one link distance or as valleys by the xi-steep method. HDBSCAN builds the hierarchy of all density levels, keeps the clusters that persist longest, and yields a per-point membership and GLOSH outlier score from the same tree. Neither implements HardClusterer, since neither produces one clustering. Both agree with scikit-learn on generated sets: core distances and the DBSCAN extraction exactly, and the labels except where tied distances leave more than one valid answer. Two properties the README states for callers: a membership probability is scaled per cluster and is not comparable between clusters, and a point whose core distance dominates its neighborhood has several equally short edges, so tie-breaking rather than the data decides which cluster takes it.
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