The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Accelerated Hierarchical Density Based Clustering
Open accessAbstract
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 epsilon. This makes accelerated HDBSCAN* the default choice for density based clustering.
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Cites
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Density-Based Clustering Based on Hierarchical Density Estimates
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BIRCH: an efficient data clustering method for very large databases
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Metadata
- Authors
- McInnes, L., & Healy, J.
- Venue
- 2017 IEEE International Conference on Data Mining Workshops (ICDMW)
- Publisher
- IEEE
- Published
- 2017-11
- Pages
- 33-42
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W2619824656
- Cited by
- 434
- References
- 29
- FWCI
- 6.35
- Type
- conference paper
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
