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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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| Autori principali: | , , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Oxford University Press
2008
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| Soggetti: | |
| Accesso online: | 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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