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dc.contributor.authorSaunders, Amanda
dc.contributor.authorAshlock, Daniel
dc.contributor.authorHoughten, Sheridan
dc.date.accessioned2023-01-05T14:28:52Z
dc.date.available2023-01-05T14:28:52Z
dc.date.issued2018-5
dc.identifier.urihttp://hdl.handle.net/10464/17137
dc.description.abstractHierarchical clustering via neighbor joining, widely used in biology, can be quite sensitive to the addition or deletion of single taxa. In an earlier study it was found that neighbor joining trees on random data were commonly quite unstable in the sense that large re-arrangements of the tree occurred when the tree was reconstructed after the deletion of a single data point. In this study, we use an evolutionary algorithm to evolve extremely stable and unstable data sets for a standard neighbor-joining algorithm and then check the stability using a novel type of clustering called bubble clustering. Bubble clustering is an instance of associator clustering. The stability measure used is based on the size of the subtree containing each pair of taxa, a quantity that provides an objective measure of a given trees hypothesis about the relatedness of taxa. It is shown experimentally that even in data sets evolved to be stable for a standard neighbor joining algorithm, bubble clustering is a significantly more stable algorithm.en_US
dc.publisherIEEE
dc.source2018 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)
dc.subjectEvolutionary algorithmen_US
dc.subjectClusteringen_US
dc.titleHierarchical clustering and tree stabilityen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/cibcb.2018.8404978
refterms.dateFOA2023-01-05T14:28:52Z


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