The DOI matches its Crossref record.
A Survey of Recent Advances in Hierarchical Clustering Algorithms
Open accessAbstract
It has often been asserted that since hierarchical clustering algorithms require pairwise interobject proximities, the complexity of these clustering procedures is at least O(N2). Recent work has disproved this by incorporating efficient nearest neighbour searching algorithms into the clustering algorithms. A general framework for hierarchical, agglomerative clustering algorithms is discussed here, which opens up the prospect of much improvement on current, widely-used algorithms. This ‘progress report’ details new algorithmic approaches in this area, and reviews recent results.
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
Cited by
A DNA-Based Registry for All Animal Species: The Barcode Index Number (BIN) System
Ratnasingham, S., & Hebert, P. D. N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066213 · Cited by 2,301
A Comprehensive Survey of Clustering Algorithms
Xu, D., & Tian, Y. (2015) · Annals of Data Science · doi:10.1007/s40745-015-0040-1 · Cited by 1,939
Rammal, R., Toulouse, G., & Virasoro, M. A. (1986) · Reviews of Modern Physics · doi:10.1103/revmodphys.58.765 · Cited by 756
Nielsen, F. (2016) · Undergraduate Topics in Computer Science · doi:10.1007/978-3-319-21903-5_8 · Cited by 494
A survey of Clustering Algorithms
Rokach, L. (2009) · Data Mining and Knowledge Discovery Handbook · doi:10.1007/978-0-387-09823-4_14 · Cited by 245
Metadata
- Authors
- Murtagh, F.
- Venue
- The Computer Journal
- Publisher
- Oxford University Press (OUP)
- Published
- 1983-11-01
- Volume
- 26
- Issue
- 4
- Pages
- 354-359
- ISSN
- 0010-4620 (ISSN-L)
- 1460-2067
- OpenAlex
- W2141807666
- Cited by
- 1,058
- References
- 0
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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: A Survey of Recent Advances in Hierarchical Clustering Algorithms. https://app.cochat.ai/papers/10.1093/comjnl/26.4.354
- The paper
- Murtagh, F. (1983). A Survey of Recent Advances in Hierarchical Clustering Algorithms. The Computer Journal. https://doi.org/10.1093/comjnl/26.4.354
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.
