The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Clustering by Passing Messages Between Data Points
Abstract
Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such “exemplars” can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called “affinity propagation,” which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.
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Cites
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Self-citation
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Cited by
Clustering by fast search and find of density peaks
Rodriguez, A., & Laio, A. (2014) · Science · doi:10.1126/science.1242072 · Cited by 4,942
WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs
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Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124
Systematic identification of genomic markers of drug sensitivity in cancer cells
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Metadata
- Authors
- Frey, B. J., & Dueck, D.
- Venue
- Science
- Published
- 2007-02-16
- Volume
- 315
- Issue
- 5814
- Pages
- 972-976
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2165232124
- Cited by
- 6,758
- References
- 3
- FWCI
- 232.85
- Type
- journal article
- Language
- en
- Open access
- closed
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