Moisl, Hermann - How to visualize high-dimensional data: a roadmap

jdmdh:5594 - Journal of Data Mining & Digital Humanities, December 23, 2020, Special issue on Visualisations in Historical Linguistics
How to visualize high-dimensional data: a roadmap

Authors: Moisl, Hermann

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.

Volume: Special issue on Visualisations in Historical Linguistics
Published on: December 23, 2020
Submitted on: June 21, 2019
Keywords: dimensionality reduction,high dimensionality,multivariate data,Data visualization,cluster analysis,[SHS]Humanities and Social Sciences


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