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dc.contributor.authorFarid, Mohsen
dc.contributor.authorMurtagh, Fionn
dc.date.accessioned2020-10-02T13:25:34Z
dc.date.available2020-10-02T13:25:34Z
dc.date.issued2017-02-06
dc.identifier.citationMurtagh, F., and Farid, M. (2017). ‘Contextualizing geometric data analysis and related data analytics: A virtual microscope for big data analytics’. Journal of Interdisciplinary Methodologies and Issues in Science, 3, pp. 1-19.en_US
dc.identifier.doi10.18713/JIMIS-010917-3-1
dc.identifier.urihttp://hdl.handle.net/10545/625220
dc.description.abstractThe relevance and importance of contextualizing data analytics is described. Qualitative characteristics might form the context of quantitative analysis. Topics that are at issue include: contrast, baselining, secondary data sources, supplementary data sources, dynamic and heterogeneous data. In geometric data analysis, especially with the Correspondence Analysis platform, various case studies are both experimented with, and are reviewed. In such aspects as paradigms followed, and technical implementation, implicitly and explicitly, an important point made is the major relevance of such work for both burgeoning analytical needs and for new analytical areas including Big Data analytics, and so on. For the general reader, it is aimed to display and describe, first of all, the analytical outcomes that are subject to analysis here, and then proceed to detail the more quantitative outcomes that fully support the analytics carried out.en_US
dc.description.sponsorshipN/Aen_US
dc.language.isoenen_US
dc.publisherLe Centre pour la Communication Scientifique Directeen_US
dc.relation.urlhttps://jimis.episciences.org/3936en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectanalytical focus; contextualization of data and information; Correspondence Analysis; Multiple Correspondence Analysis; dimensionality reduction; mental healthen_US
dc.titleContextualizing geometric data analysis and related data analytics: A virtual microscope for big data analyticsen_US
dc.typeArticleen_US
dc.contributor.departmentUniversity of Derbyen_US
dc.contributor.departmentUniversity of Huddersfielden_US
dc.identifier.journalJournal of Interdisciplinary Methodologies and Issues in Sciencesen_US
dcterms.dateAccepted2016-02-12
refterms.dateFOA2020-10-02T13:25:35Z
dc.author.detail784680en_US


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