Estimating the Number of Clusters in a Data Set Via the Gap Statistic
Abstract
Summary: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
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The cell cycle often has an enormous impact on cellular gene expression, and this impact can extend to other processes and be affected by them.
“The cell cycle often has an enormous impact on cellular gene expression146,149,150 (although some87 have challenged this view), but this impact can extend to other processes, and in turn be affected by them.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [150]
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- Authors
- Tibshirani, R., Walther, G., & Hastie, T.
- Affiliations
- Stanford University (US)
- Corresponding author
- Tibshirani, Robert
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-07-01
- Volume
- 63
- Issue
- 2
- Pages
- 411-423
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W101836494
- Cited by
- 6,205
- References
- 0
- Citations by year
- 2026: 372 · 2025: 399 · 2024: 460 · 2023: 456 · 2022: 451 · 2021: 542 · 2020: 493 · 2019: 419 · 2018: 398 · 2017: 329 · 2016: 279 · 2015: 215 · 2014: 230 · 2013: 175 · 2012: 177
- FWCI
- 39.46
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- Journal of the Royal Statistical Society Series B (Statistical Methodology) (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Science Foundation (9971405, 9803645)
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- gap statistic, number of clusters, cluster detection, K-means clustering, hierarchical clustering, model selection
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