SFB-TRR 161 A03 "Quantification of Visual Analytics Transformations and Mappings"

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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41 to 50 of 55 Results

uniform-100.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 14.3 KB - 9 Variables, 100 Observations - UNF:6:UOGv/0HDWoM5g2/u8CzeSg== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 100 records.
uniform-200.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 28.5 KB - 9 Variables, 200 Observations - UNF:6:NMP7PGDcDMEe7v0riGb5uQ== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 200 records.
uniform-400.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 57.0 KB - 9 Variables, 400 Observations - UNF:6:YDkCJkbQEHrZ8rtkXyHDLQ== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 400 records.
uniform-800.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 114.0 KB - 9 Variables, 800 Observations - UNF:6:Xckxodnqrq7XKB7pkEERDQ== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 800 records.
variance-100.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 13.5 KB - 6 Variables, 150 Observations - UNF:6:4FduUnJwRNYGTwDWaUbWVw== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 100 records.
variance-200.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 22.5 KB - 6 Variables, 250 Observations - UNF:6:UEtK771CeC5HA0oYlouNfw== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 200 records.
variance-400.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 40.4 KB - 6 Variables, 450 Observations - UNF:6:9HyN/2jlcVdPuuq52hIwpQ== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 400 records.
variance-800.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 76.4 KB - 6 Variables, 850 Observations - UNF:6:dHHpAC3qiIpFizO+EWIYiA== This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 800 records.
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. All datasets are purely categorical datasets.
dawson.tab Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets" Tabular Data - 81.1 KB - 4 Variables, 2201 Observations - UNF:6:n0hJE5dgUmOgW4TXbPH6jw== The well-known titanic dataset from Dawson R. J. M. (1995) [Daw95]. [Daw95] Dawson R. J. M.:The "unusual episode" data revisited, 1995. http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18.

uniform-100.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 14.3 KB - 9 Variables, 100 Observations -

This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 100 records.

uniform-200.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 28.5 KB - 9 Variables, 200 Observations -

This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 200 records.

uniform-400.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 57.0 KB - 9 Variables, 400 Observations -

This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 400 records.

uniform-800.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 114.0 KB - 9 Variables, 800 Observations -

This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe uniform noise. This dataset has 800 records.

variance-100.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 13.5 KB - 6 Variables, 150 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 100 records.

variance-200.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 22.5 KB - 6 Variables, 250 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 200 records.

variance-400.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 40.4 KB - 6 Variables, 450 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 400 records.

variance-800.tab

Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

Tabular Data - 76.4 KB - 6 Variables, 850 Observations -

This file contains a synthetic dataset. It has five numeric dimensions. The data records of this dataset describe three classes also forming clusters in all dimensions. This dataset has 800 records.

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. All datasets are purely categorical datasets.

dawson.tab

Jun 30, 2022 - Replication Data for: "ParSetgnostics: Quality Metrics for Parallel Sets"

Tabular Data - 81.1 KB - 4 Variables, 2201 Observations -

The well-known titanic dataset from Dawson R. J. M. (1995) [Daw95]. [Daw95] Dawson R. J. M.:The "unusual episode" data revisited, 1995. http://jse.amstat.org/v3n3/datasets.dawson.html, last accessed 2020-09-18.

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