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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The Categorical Data Map - Replication Data
Jan 26, 2024
Dennig, Frederik L.; Joos, Lucas; Paetzold, Patrick; Blumberg, Daniela; Deussen, Oliver; Keim, Daniel; Fischer, Maximilian T., 2024, "The Categorical Data Map - Replication Data", https://doi.org/10.18419/darus-3372, DaRUS, V1, UNF:6:4NrkBxJKpeeQqsRmi8XRPw== [fileUNF]
Source code and datasets used for our experiments are shared for replication purposes along our publication "The Categorical Data Map". We describe each of the six datasets individually on a per-file basis. All datasets are purely nominal datasets.
Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"
Mar 6, 2023
Pomerenke, David; Dennig, Frederik L.; Keim, Daniel; Fuchs, Johannes; Blumenschein, Michael, 2022, "Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"", https://doi.org/10.18419/darus-3060, DaRUS, V2, UNF:6:UBKuKSiQ9Yl4rH7r00rY3g== [fileUNF]
This is the replication data for our publication "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters." It contains the datasets and the code used to render optimized Parallel Coordinate Plots. We used the following 36 datasets for ou...
Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"
Jun 30, 2022
Dennig, Frederik L.; Fischer, Maximilian T.; Blumenschein, Michael; Fuchs, Johannes; Keim, Daniel; Dimara, Evanthia, 2022, "Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"", https://doi.org/10.18419/darus-2869, DaRUS, V1, UNF:6:mmqXqGYXSM0L6g/xQCjGUg== [fileUNF]
This is the replication data for our research article "ParSetgnostics: Quality Metrics for Parallel Sets." It contains the datasets used to obtain optimized Parallel Sets visualizations. We used the following six datasets for our experiments, which we describe on a per-file basis...
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