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arXiv:1705.07321stat.ML

Accelerated Hierarchical Density Clustering

Leland McInnes, John Healy

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Abstract

We present an accelerated algorithm for hierarchical density based clustering. Our new algorithm improves upon HDBSCAN*, which itself provided a significant qualitative improvement over the popular DBSCAN algorithm. The accelerated HDBSCAN* algorithm provides comparable performance to DBSCAN, while supporting variable density clusters, and eliminating the need for the difficult to tune distance scale parameter. This makes accelerated HDBSCAN* the default choice for density based clustering. Library available at: https://github.com/scikit-learn-contrib/hdbscan

arXiv comments
Journal reference
2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017, pp. 33-42
DOI
doi:10.1109/ICDMW.2017.12
Subjects
stat.MLMachine Learning (Statistics)

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  1. A gentle introduction to HDBSCAN and density-based clustering 1 comment

    • This is sorta true, but not quite. Leland McInnes and John Healy (the creators of UMAP), do in-fact have an amazing paper on HDBSCAN, but it's not inventing it. In their paper, https://arxiv.org/pdf/1705.07321.pdf, they introduce AHDBSCAN which is a great extension of HDBSCAN to…

      bbischofView on HNParent context

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