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Article Dans Une Revue Journal of Data Mining and Digital Humanities Année : 2020

How to visualize high-dimensional data: a roadmap

Hermann Moisl
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Résumé

Discovery of the chronological or geographical distribution of collections of historical text can be more reliable when based on multivariate rather than on univariate data because multivariate data provide a more complete description. Where the data are high-dimensional, however, their complexity can defy analysis using traditional philological methods. The first step in dealing with such data is to visualize it using graphical methods in order to identify any latent structure. If found, such structure facilitates formulation of hypotheses which can be tested using a range of mathematical and statistical methods. Where, however, the dimensionality is greater than 3, direct graphical investigation is impossible. The present discussion presents a roadmap of how this obstacle can be overcome, and is in three main parts: the first part presents some fundamental data concepts, the second describes an example corpus and a high-dimensional data set derived from it, and the third outlines two approaches to visualization of that data set: dimensionality reduction and cluster analysis.
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Dates et versions

hal-02145440 , version 1 (02-06-2019)
hal-02145440 , version 2 (17-12-2020)

Identifiants

Citer

Hermann Moisl. How to visualize high-dimensional data: a roadmap. Journal of Data Mining and Digital Humanities, In press, Special Issue on Visualisations in Historical Linguistics, Special issue on Visualisations in Historical Linguistics, pp.1-19. ⟨10.46298/jdmdh.5594⟩. ⟨hal-02145440v2⟩
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