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- W3020965650 abstract "We present a new algorithm for the widely used density-based clustering method dbscan. For a set of [Formula: see text] points in [Formula: see text] our algorithm computes the dbscan-clustering in [Formula: see text] time, irrespective of the scale parameter [Formula: see text] (and assuming the second parameter MinPts is set to a fixed constant, as is the case in practice). Experiments show that the new algorithm is not only fast in theory, but that a slightly simplified version is competitive in practice and much less sensitive to the choice of [Formula: see text] than the original dbscan algorithm. We also present an [Formula: see text] randomized algorithm for hdbscan in the plane — hdbscan is a hierarchical version of dbscan introduced recently — and we show how to compute an approximate version of hdbscan in near-linear time in any fixed dimension." @default.
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- W3020965650 date "2019-03-01" @default.
- W3020965650 modified "2023-09-27" @default.
- W3020965650 title "Faster DBSCAN and HDBSCAN in Low-Dimensional Euclidean Spaces" @default.
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- W3020965650 doi "https://doi.org/10.1142/s0218195919400028" @default.
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