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Density-Based Clustering Based on Hierarchical Density Estimates
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“Small values produce numerous small clusters potentially appearing only at extreme density thresholds, whereas larger values merge smaller groups into fewer, more stable clusters, that can potentially hide the variability of the patterns observed.”
From Ecological legacies of pre-columbian settlements evident in palm clusters of neotropical mountain forests (2026) · cites this paper as [65]
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“HDBSCAN is adept at clustering outputs from LLMs due to its ability to manage clusters with varied densities, autonomously identify the number of clusters, and filter out noise as outlier data.”
From Large language model trained on clinical oncology data predicts cancer progression (2025) · cites this paper as [54]
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UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
(1996) · Cited by 20,179
Self-citationHubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987
(1975) · CERN Document Server (European Organization for Nuclear Research) · Cited by 5,958
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Self-citationThe Amsterdam Library of Object Images
Geusebroek, J., Burghouts, G. J., & Smeulders, A. W. M. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000042993.50813.60 · Cited by 919
Kriegel, H., Kröger, P., Sander, J., & Zimek, A. (2011) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.30 · Cited by 900
Self-citationFast and effective text mining using linear-time document clustering
Larsen, B., & Aone, C. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312186 · Cited by 869
Model-based clustering and data transformations for gene expression data
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E., & Ruzzo, W. L. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.10.977 · Cited by 869
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Hunt, E. L., Cantat-Gaudin, T., Anders, F., Spina, L., Cavallo, L., Castro-Ginard, A., Belokurov, V. A., Brown, A. G. A., Casey, A. R., Drimmel, R., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202452614 · Cited by 8
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Metadata
- Authors
- Campello, R. J. G. B., Moulavi, D., & Sander, J.
- Affiliations
- University of Alberta (CA)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Volume
- 7819
- Pages
- 160-172
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W151377110
- Cited by
- 2,680
- References
- 27
- Citations by year
- 2026: 413 · 2025: 497 · 2024: 404 · 2023: 337 · 2022: 271 · 2021: 253 · 2020: 181 · 2019: 148 · 2018: 84 · 2017: 50 · 2016: 24 · 2015: 9 · 2014: 9
- FWCI
- 9.26
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- ResearchOnline - JCU (James Cook University) (submittedVersion)
- Lecture Notes in Artificial Intelligence
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Keywords
- density-based clustering, hierarchical clustering, density limit
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