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Efficient algorithms for accurate hierarchical clustering of huge datasets: tackling the entire protein space
Motivation: UPGMA (average linking) is probably the most popular algorithm for hierarchical data clustering, especially in computational biology. However, UPGMA requires the entire dissimilarity matrix in memory. Due to this prohibitive requirement, UPGMA is not scalable to very large datasets. Appl...
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| 主要な著者: | , , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Oxford University Press
2008
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2718652/ https://ncbi.nlm.nih.gov/pubmed/18586742 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1093/bioinformatics/btn174 |
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