Cited by 2,116Gene expression and cancer classificationAdvanced Clustering Algorithms ResearchBayesian Methods and Mixture Models

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Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data

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Monti, Stefano, Tamayo, Pablo, Mesirov, Jill, Golub, Todd·2003·Machine Learning

DOI 10.1023/a:1023949509487

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Authors: Monti, S., Tamayo, P., Mesirov, J., & Golub, T.
Affiliations: Massachusetts Institute of Technology (US)
ORCID: Monti, Stefano
Tamayo, Pablo
Mesirov, Jill
Golub, Todd
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 2003-07
Volume: 52
Issue: 1-2
Pages: 91-118
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1023/a:1023949509487
OpenAlex: W1548779692
Cited by: 2,116
References: 49
Citations by year: 2026: 134 · 2025: 113 · 2024: 155 · 2023: 133 · 2022: 139 · 2021: 192 · 2020: 123 · 2019: 112 · 2018: 120 · 2017: 100 · 2016: 85 · 2015: 94 · 2014: 78 · 2013: 77 · 2012: 80
FWCI: 8.98
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Machine Learning (publishedVersion)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
Keywords: consensus clustering, gene expression microarrays, cluster validation, cluster stability, resampling, cluster visualization, optimal number of clusters, K-means clustering, self-organizing maps, Bayesian clustering, sensitivity to initial conditions

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