The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A new partitioning around medoids algorithm
Abstract
Kaufman and Rousseeuw (1990) proposed a clustering algorithm Partitioning Around Medoids (PAM) which maps a distance matrix into a specified number of clusters. A particularly nice property is that PAM allows clustering with respect to any specified distance metric. In addition, the medoids are robust representations of the cluster centers, which is particularly important in the common context that many elements do not belong well to any cluster. Based on our experience in clustering gene expression data, we have noticed that PAM does have problems recognizing relatively small clusters in situations where good partitions around medoids clearly exist. In this paper, we propose to partition around medoids by maximizing a criteria "Average Silhouette" defined by Kaufman and Rousseeuw (1990). We also propose a fast-to-compute approximation of "Average Silhouette". We implement these two new partitioning around medoids algorithms and illustrate their performance relative to existing partitioning methods in simulations.
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
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Cited by
A simple and fast algorithm for K-medoids clustering
Park, H.-S., & Jun, C.-H. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.01.039 · Cited by 2,104
Machine learning and its applications to biology
Tarca, A. L., Carey, V. J., Chen, X.-W., Romero, R., & Drăghici, S. (2007) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030116 · Cited by 656
Network Hubs Buffer Environmental Variation in Saccharomyces cerevisiae
Levy, S. F., & Siegal, M. L. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060264 · Cited by 286
Self-Other Representation in the Social Brain Reflects Social Connection
Courtney, A. L., & Meyer, M. L. (2020) · The Journal of Neuroscience · doi:10.1523/jneurosci.2826-19.2020 · Cited by 115
Metadata
- Authors
- Van der Laan, M., Pollard, K., & Bryan, J.
- Venue
- Journal of Statistical Computation and Simulation
- Publisher
- Informa UK Limited
- Published
- 2003-08
- Volume
- 73
- Issue
- 8
- Pages
- 575-584
- ISSN
- 0094-9655 (ISSN-L)
- 1563-5163
- OpenAlex
- W1967702193
- Cited by
- 287
- References
- 1
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- closed
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 new partitioning around medoids algorithm. https://app.cochat.ai/papers/10.1080/0094965031000136012
- The paper
- Van der Laan, M., Pollard, K., & Bryan, J. (2003). A new partitioning around medoids algorithm. Journal of Statistical Computation and Simulation. https://doi.org/10.1080/0094965031000136012
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.
