Murtagh, Fionn and Farid, Mohsen - Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics

jimis:3936 - Journal of Interdisciplinary Methodologies and Issues in Sciences, 19 septembre 2017, Contextualisation Numérique
Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics

Auteurs : Murtagh, Fionn and Farid, Mohsen

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.


Source : oai:arXiv.org:1611.09948
DOI : 10.18713/JIMIS-010917-3-1
Volume : Contextualisation Numérique
Publié le : 19 septembre 2017
Déposé le : 2 décembre 2016
Mots-clés : Computer Science - Artificial Intelligence,Computer Science - Computers and Society,62H30, 68P01, 6207,G.3,H.2.8,I.2.1


Exporter

Partager

Statistiques de consultation

Cette page a été consultée 142 fois.
Le PDF de cet article a été téléchargé 35 fois.