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The Persistence of Data: A Road Map

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dc.contributor.author Pothagoni, Shrunal
dc.date.accessioned 2022-05-08T15:07:59Z
dc.date.available 2022-05-08T15:07:59Z
dc.date.issued 2022-04-22
dc.identifier.uri http://hdl.handle.net/1920/12810
dc.description Honors Thesis en_US
dc.description.abstract The purpose of data mining is to use advanced mathematical and statistical techniques to extract quantitative information from large data sets. These tools are incredibly powerful and in conjunction with machine learning algorithms allow for extremely accurate pattern prediction. However, there are various datasets that have qualitative properties that cannot be discerned using classic data mining techniques. Topological Data Analysis (TDA) is a field developed within the last two decades that uses methods in topology to extract such qualitative features. In this paper we will study how to use abstract simplicial complexes on point cloud data sets to find their most ‘optimal’ topology using computational homology. en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject persistence theory en_US
dc.subject TDA en_US
dc.subject computational homology en_US
dc.title The Persistence of Data: A Road Map en_US
dc.type Thesis en_US


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