DOE OSTI · code-184396
Clustering at Massive Scale
Abstract
ClaMS provides hierarchical clustering technology for use on massive, high-dimensional datasets that require distributed memory for processing. The algorithm employed is inspired by the popular HDBSCAN algorithm but makes use of computational kernels better suited for distributed computing. ClaMS is built on scalable nearest neighbor graph construction, metric forest completion, and approximate minimum spanning tree techniques.
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Stanley, ThomasA [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Li, GraceJ [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Priest, BenjaminW [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Steil, TrevorW [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)], Iwabuchi, Keita [Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)]. 2025-10-30. Clustering at Massive Scale. https://doi.org/10.11578/dc.20260629.1
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