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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11 to 20 of 55 Results

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 our experiments, which we describe on a per-file basis. All datasets are...
pcp-line-adjustment-master.zip Mar 6, 2023 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" ZIP Archive - 1.4 MB - MD5: 01052b403a5baf507edfb9d7b0b4be80 Source code of the project with documentation to build the research prototype. Also available at https://github.com/davidpomerenke/slope.
correlations-100.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 9.0 KB - 6 Variables, 100 Observations - UNF:6:B6Qnhv0vjfsgXsgj4Cw3sA== This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong inverse correlation. This dataset has 100 records.
correlations-200.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 18.0 KB - 6 Variables, 200 Observations - UNF:6:m3UG8RKHop20WoVRX86OwQ== This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong inverse correlation. This dataset has 200 records.
correlations-400.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 36.0 KB - 6 Variables, 400 Observations - UNF:6:4ssZpvmFWXvwIwNYAEczCQ== This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong inverse correlation. This dataset has 400 records.
correlations-800.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 71.9 KB - 6 Variables, 800 Observations - UNF:6:S58mxxJ0M5mXhtwEBd4wNQ== This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong inverse correlation. This dataset has 800 records.
linear-100.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 42.8 KB - 9 Variables, 300 Observations - UNF:6:PdSrWEez3y/v+I9vh1wCVA== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe linear noise. This dataset has 100 records.
linear-200.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 85.3 KB - 9 Variables, 600 Observations - UNF:6:0STgAq7ajnWqiaHktdy2Lg== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe linear noise. This dataset has 200 records.
linear-400.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 171.1 KB - 9 Variables, 1200 Observations - UNF:6:76ZPxinznbNpdEI0XMPw8Q== This file contains a synthetic dataset. It has eight numeric dimensions. The data records of this dataset describe linear noise. This dataset has 400 records.
linear-800.tab Sep 27, 2022 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters" Tabular Data - 340.9 KB - 9 Variables, 2400 Observations - UNF:6:N76YMbDIK33lATS3I2ZKTg== This file contains a synthetic dataset. It has eight dimensions. The data records of this dataset describe linear noise. This dataset has 800 records.

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 our experiments, which we describe on a per-file basis. All datasets are...

pcp-line-adjustment-master.zip

Mar 6, 2023 - Replication Data for: "Slope-Dependent Rendering of Parallel Coordinates to Reduce Density Distortion and Ghost Clusters"

ZIP Archive - 1.4 MB -

Source code of the project with documentation to build the research prototype. Also available at https://github.com/davidpomerenke/slope.

correlations-100.tab

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

Tabular Data - 9.0 KB - 6 Variables, 100 Observations -

This file contains a synthetic dataset. It has five numeric dimensions with the following properties: Dimensions 1 and 2 are strongly correlated, dimensions 2 and 3 have a strong inverse correlation, dimensions 2 and 4 are strongly correlated, and dimensions 4 and 5 have a strong inverse correlation. This dataset has 100 records.

correlations-200.tab

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

Tabular Data - 18.0 KB - 6 Variables, 200 Observations -

correlations-400.tab

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

Tabular Data - 36.0 KB - 6 Variables, 400 Observations -

correlations-800.tab

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

Tabular Data - 71.9 KB - 6 Variables, 800 Observations -

linear-100.tab

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

Tabular Data - 42.8 KB - 9 Variables, 300 Observations -

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

linear-200.tab

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

Tabular Data - 85.3 KB - 9 Variables, 600 Observations -

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

linear-400.tab

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

Tabular Data - 171.1 KB - 9 Variables, 1200 Observations -

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

linear-800.tab

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

Tabular Data - 340.9 KB - 9 Variables, 2400 Observations -

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

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