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Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches

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dc.contributor.author Boyack, Kevin W.
dc.contributor.author Newman, David
dc.contributor.author Duhon, Russell J.
dc.contributor.author Klavans, Richard
dc.contributor.author Patek, Michael
dc.contributor.author Biberstine, Joseph R.
dc.contributor.author Schijvenaars, Bob
dc.contributor.author Skupin, André
dc.contributor.author Ma, Nianli
dc.contributor.author Börner, Katy
dc.date.accessioned 2011-09-12T18:12:22Z
dc.date.available 2011-09-12T18:12:22Z
dc.date.issued 2011
dc.identifier.citation Boyack, Kevin W., Newman, David, Duhon, Russell Jackson, Klavans, Richard, Patek, Michael, Biberstine, Joseph R., Schijvenaars, Bob, Skupin, Andre, Ma, Nianli & Börner, Katy. (2011). Clustering More Than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches. PLoS ONE. Vol. 6, 1-11. en
dc.identifier.uri http://dx.doi.org/10.1371/journal.pone.0018029 en
dc.identifier.uri http://hdl.handle.net/2022/13472
dc.description.abstract We investigate the accuracy of different similarity approaches for clustering over two million biomedical documents. Clustering large sets of text documents is important for a variety of information needs and applications such as collection management and navigation, summary and analysis. The few comparisons of clustering results from different similarity approaches have focused on small literature sets and have given conflicting results. Our study was designed to seek a robust answer to the question of which similarity approach would generate the most coherent clusters of a biomedical literature set of over two million documents. en
dc.language.iso en_US en
dc.publisher Public Library of Science en
dc.relation.isversionof The original publication of this article is available at http://www.plosone.org/home.action. en
dc.title Clustering More than Two Million Biomedical Publications: Comparing the Accuracies of Nine Text-Based Similarity Approaches en
dc.type Article en


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