High-dimensional data analysis requires dealing with numerous challenges, such as selecting meaningful dimensions, finding relevant projections, and removing noise. As a result, the extraction of relevant and meaningful information from high-dimensional data is a difficult problem. This project aims at advancing the field of quality-metric-driven data visualisation with the central research question of how to quantify the quality of transformations and mappings of high-dimensional data for visual analytics.
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schaetzle.csv
Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"
Tabular Data - 15.7 KB - 2 Variables, 1021 Observations - UNF:6:E70xYkGzgv0fjFoXL7T9uA==
A categorical dataset reconstructed from Schätzle et al. [SDB*19] describing language change in Icelandic. It is manually reconstructed from the Parallel Sets visualization in Figure 3 of the publication. The reconstruction method is described in the Process Metadata. The publication does not provide a source for the underlying data. [SDB*19] Chri...
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