Contextualizing Geometric Data Analysis and Related Data Analytics: A Virtual Microscope for Big Data Analytics
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.
- Benzécri J.-P. (1979). Sur les calculs des taux d’inertie dans l’analyse d’un questionnaire. Les Cahiers de l’Analyse des Données IV, 377–378.
- Living conditions, interviewer effects and perceived well-being of the elderly. A Multiple Correspondence Analysis approach. In A. Ferligoj and A. Mrvar (Eds.), Developments in Social Science Methodology, pp. 125–146. FDV.
- Making the Case for the Social Sciences, No. 9 Mental Wellbeing. BACP, British Association for Counselling and Psychotherapy. 28 pp. http://www.acss.org.uk/wp-content/uploads/2013/09/Making-the-Case-9-Mental-Wellbeing-Web.pdf.
- Greenacre M. (1993). Correspondence Analysis in Practice. Academic Press.
- Keiding N., Louis T. (2016). Perils and potentials of self-selected entry to epidemiological studies and surveys. Journal of the Royal Statistical Society, Series A 179, Part 2, 319–376. Discussants include F. Murtagh.
- Time for mental health to come out of the shadows. The Lancet 287, 2274–2275.
- Le Roux B. (2014). Analyse Géométrique des Données Multidimensionnelles. Dunod.
- Lebaron F., Roux B. L. (2015). La Méthodologie de Pierre Bourdieu en Action: Espace Culturel, Espace Social et Analyse des Données. Dunod.
- Murtagh F. (2005). Correspondence Analysis and Data Coding with R and Java. Chapman and Hall/CRC Press.
- Murtagh F. (2010). The correspondence analysis platform for uncovering deep structure in data and information. Computer Journal 53, 304–315. Sixth Boole Lecture.
- Murtagh F. (2016a). The geometry and topology of data and information for analytics of processes and behaviours: Building on Bourdieu and addressing new societal challenges. In J. Blasius, B. Le Roux, F. Lebaron, and A. Schmitz (Eds.), Empirical Investigation of Social Space. Submitted, preprint: https://arxiv.org/abs/1705.08503.
- Murtagh F. (2016b). Semantic mapping: towards contextual and trend analysis of behaviours and practices. In K. Balog, L. Cappellato, N. Ferro, and C. MacDonald (Eds.), Working Notes of CLEF 2016 – Conference and Labs of the Evaluation Forum, Évora, Portugal, 5-8 September, 2016, pp. 1207–1225. http://ceur-ws.org/Vol-1609.
- Murtagh F. (2017). Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics. Chapman and Hall/CRC Press.
- Qualitative judgement of research impact: Domain taxonomy as a fundamental framework for judgement of the quality of research. Journal of Classification. In press, preprint: https://arxiv.org/abs/1607.03200.
- NHS (2009). Adult Psychiatric Morbidity in England – 2007, Results of a household survey (National Statistics). PDF file, 174 pages. HSCIC, Health and Social Care Information Centre (NHS, National Health Service, UK). http://www.hscic.gov.uk/pubs/psychiatricmorbidity07.
- Solène B., Le Roux B. (2016). Combinatorial typicality test in geometric data analysis. preprint.
- Wessel M. (2016). You don’t need big data – you need the right data. Harvard Business Review. 3 Nov. 2016. https://hbr.org/2016/11/you-dont-need-big-data-you-need-the-right-data.
- A predictive model for assistive technology adoption for people with dementia. IEEE Journal of Biomedical and Health Informatics 18, 375–383.
- Probabilistic learning from incomplete data for recognition of activities of daily living in smart homes. IEEE Transactions on Information Technology in Biomedicine 16, 454–462.
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