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Extracting insights from the shape of complex data using topology
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2013 (English)In: Scientific Reports, ISSN 2045-2322, Vol. 3Article in journal (Refereed) Published
Abstract [en]

This paper applies topological methods to study complex high dimensional data sets by extracting shapes (patterns) and obtaining insights about them. Our method combines the best features of existing standard methodologies such as principal component and cluster analyses to provide a geometric representation of complex data sets. Through this hybrid method, we often find subgroups in data sets that traditional methodologies fail to find. Our method also permits the analysis of individual data sets as well as the analysis of relationships between related data sets. We illustrate the use of our method by applying it to three very different kinds of data, namely gene expression from breast tumors, voting data from the United States House of Representatives and player performance data from the NBA, in each case finding stratifications of the data which are more refined than those produced by standard methods.

Place, publisher, year, edition, pages
Nature Publishing Group, 2013. Vol. 3
Keyword [en]
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Computational Mathematics
URN: urn:nbn:se:kth:diva-150371DOI: 10.1038/srep01236ISI: 000314710400001OAI: diva2:742574

QC 20140908

Available from: 2014-09-02 Created: 2014-09-02 Last updated: 2014-09-08Bibliographically approved

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Vejdemo-Johansson, Mikael
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