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In search of more stable hierarchical trees: devising new algorithms to improve upon the stability of neighbor joining

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dc.contributor.advisor Ashlock, Daniel
dc.contributor.author Saunders, Amanda
dc.date.accessioned 2017-09-05T14:51:22Z
dc.date.available 2017-09-05T14:51:22Z
dc.date.copyright 2017-06
dc.date.created 2017-08-30
dc.date.issued 2017-09-05
dc.identifier.uri http://hdl.handle.net/10214/11502
dc.description.abstract The standard method of constructing hierarchical trees – neighbour joining – while commonly used, has some major flaws. For most data sets not derived from a common descent process, it produces trees that are highly unstable. Adding or deleting a point can cause dramatic shifts in the tree topology. The degree of this shift can be measured by calculating the distance between trees. To provide an alternative to neighbour joining, bubble clustering was devised. Instead of using the simple Euclidean distance between points, the connections between points are established by repeated sampling. A multi-dimensional sphere is placed into the data space and the association between all points within the sphere is increased. Once this is complete the resultant matrix is converted into a tree by sequentially joining the most associated points. The new algorithm proved to generate more stable trees compared to neighbour joining when applied to random or real data. en_US
dc.description.sponsorship NSERC The Leading Edge Fund en_US
dc.language.iso en en_US
dc.subject hierarchical clustering en_US
dc.subject clustering en_US
dc.subject neighbor joinging en_US
dc.subject stability en_US
dc.subject algorithm en_US
dc.subject bioinformatics en_US
dc.title In search of more stable hierarchical trees: devising new algorithms to improve upon the stability of neighbor joining en_US
dc.type Thesis en_US
dc.degree.programme Bioinformatics en_US
dc.degree.name Master of Science en_US
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