The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
dbscan: Fast Density-Based Clustering with R
Open accessAbstract
This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Package dbscan uses advanced open-source spatial indexing data structures implemented in C++ to speed up computation. An important advantage of this implementation is that it is up-to-date with several improvements that have been added since the original algorithms were publications (e.g., artifact corrections and dendrogram extraction methods for OPTICS). We provide a consistent presentation of the DBSCAN and OPTICS algorithms, and compare dbscan's implementation with other popular libraries such as the R package fpc, ELKI, WEKA, PyClustering, SciKit-Learn, and SPMF in terms of available features and using an experimental comparison.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
(1967) · Cited by 23,719
(1996) · Cited by 20,179
The WEKA data mining software: an update
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,199
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Multidimensional binary search trees used for associative searching
Bentley, J. L. (1975) · Communications of the ACM · doi:10.1145/361002.361007 · Cited by 7,520
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344 · cited 1×
“A detailed review of these clustering algorithms is provided in Kaufman and Rousseeuw (1990), Jain, Murty, and Flynn (1999), and the more recent review by Aggarwal and Reddy (2013).”
MethodLOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Cited by
Bandara, K., Bergmeir, C., & Smyl, S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112896 · Cited by 435
GENESPACE tracks regions of interest and gene copy number variation across multiple genomes
Lovell, J. T., Sreedasyam, A., Schranz, M. E., Wilson, M., Carlson, J. W., Harkess, A., Emms, D., Goodstein, D. M., & Schmutz, J. (2022) · eLife · doi:10.7554/elife.78526 · Cited by 391
Durably reducing conspiracy beliefs through dialogues with AI
Costello, T. H., Pennycook, G., & Rand, D. G. (2024) · Science · doi:10.1126/science.adq1814 · Cited by 354
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01664-3 · Cited by 282
Breast tumours maintain a reservoir of subclonal diversity during expansion
Minussi, D. C., Nicholson, M. D., Ye, H., Davis, A., Wang, K., Baker, T., Tarabichi, M., Sei, E., Du, H., Rabbani, M., et al. (2021) · Nature · doi:10.1038/s41586-021-03357-x · Cited by 256
Metadata
- Authors
- Hahsler, M., Piekenbrock, M., & Doran, D.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2019
- Volume
- 91
- Issue
- 1
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2982437619
- Cited by
- 802
- References
- 59
- FWCI
- 26.48
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: dbscan: Fast Density-Based Clustering with R. https://app.cochat.ai/papers/10.18637/jss.v091.i01
- The paper
- Hahsler, M., Piekenbrock, M., & Doran, D. (2019). dbscan: Fast Density-Based Clustering with R. Journal of Statistical Software. https://doi.org/10.18637/jss.v091.i01
Data sources
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.
