hdbscan: Hierarchical density based clustering
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UMAP and HDBSCAN identified 158 distinct semantic clusters from 226,938 posts.
“we applied UMAP dimensionality reduction and HDBSCAN clustering 38 to 768dimensional embeddings of all 226,938 posts, identifying 158 distinct semantic clusters (Fig. S3).”
From The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies (2026) · cites this paper as [38]
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References 4 · Cited by 2,833
- Authors
- McInnes, L., Healy, J., & Astels, S.
- Venue
- The Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2017-03-21
- Volume
- 2
- Issue
- 11
- Pages
- 205
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W2601243251
- Cited by
- 2,833
- References
- 4
- Citations by year
- 2026: 613 · 2025: 605 · 2024: 456 · 2023: 387 · 2022: 271 · 2021: 219 · 2020: 156 · 2019: 81 · 2018: 39 · 2017: 6
- Type
- software paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- The Journal of Open Source Software (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Topological and Geometric Data Analysis
- Keywords
- HDBSCAN, density-based clustering, clustering, R package
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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