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Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics

Murtagh, Fionn and Farid, Mohsen (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. 1-18. ISSN 2430-3038

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Abstract

The 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.

Item Type: Article
Additional Information: In JIMIS journal special issue: Digital contextualization.
Uncontrolled Keywords: analytical focus, contextualization of data and information, Correspondence Analysis, Multiple Correspondence Analysis, dimensionality reduction, mental health
Subjects: H Social Sciences > HA Statistics
H Social Sciences > HV Social pathology. Social and public welfare
Q Science > QA Mathematics
Q Science > QA Mathematics > QA76 Computer software
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Schools: School of Computing and Engineering
Related URLs:
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Depositing User: Fionn Murtagh
Date Deposited: 26 Jun 2017 09:46
Last Modified: 26 Jun 2017 18:33
URI: http://eprints.hud.ac.uk/id/eprint/32310

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