Angle-uniform parallel coordinates
Abstract We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a c...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2023-03-01
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| Series: | Computational Visual Media |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s41095-022-0291-7 |
| _version_ | 1797822772518322176 |
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| author | Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang |
| author_facet | Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang |
| author_sort | Kaiyi Zhang |
| collection | DOAJ |
| description | Abstract We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations. To address this issue, we introduce a transformation that bounds all points horizontally using an angle-uniform mapping and shrinks them vertically in a structure-preserving fashion; polygonal lines become smooth curves and a symmetric representation of data correlations is achieved. We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing. Our method enables accurate visual pattern interpretation of data correlations, and its data-independent nature makes it applicable to all multidimensional datasets. The usefulness of our method is demonstrated using examples of synthetic and real-world datasets. |
| first_indexed | 2024-03-13T10:14:06Z |
| format | Article |
| id | doaj.art-ee06f458c5534fc6ba7ac5dd006c9658 |
| institution | Directory Open Access Journal |
| issn | 2096-0433 2096-0662 |
| language | English |
| last_indexed | 2024-03-13T10:14:06Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Computational Visual Media |
| spelling | doaj.art-ee06f458c5534fc6ba7ac5dd006c96582023-05-21T11:23:07ZengSpringerOpenComputational Visual Media2096-04332096-06622023-03-019349551210.1007/s41095-022-0291-7Angle-uniform parallel coordinatesKaiyi Zhang0Liang Zhou1Lu Chen2Shitong He3Daniel Weiskopf4Yunhai Wang5School of Computer Science and Technology, Shandong UniversityNational Institute of Health Data Science, Peking UniversitySchool of Computer Science and Technology, Shandong UniversitySchool of Computer Science and Technology, Shandong UniversityVisualization Research Center (VISUS), University of StuttgartSchool of Computer Science and Technology, Shandong UniversityAbstract We present angle-uniform parallel coordinates, a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot. Despite being a common method for visualizing multidimensional data, parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations. To address this issue, we introduce a transformation that bounds all points horizontally using an angle-uniform mapping and shrinks them vertically in a structure-preserving fashion; polygonal lines become smooth curves and a symmetric representation of data correlations is achieved. We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing. Our method enables accurate visual pattern interpretation of data correlations, and its data-independent nature makes it applicable to all multidimensional datasets. The usefulness of our method is demonstrated using examples of synthetic and real-world datasets.https://doi.org/10.1007/s41095-022-0291-7parallel coordinatesmultidimensional datadeformationcorrelations |
| spellingShingle | Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang Angle-uniform parallel coordinates Computational Visual Media parallel coordinates multidimensional data deformation correlations |
| title | Angle-uniform parallel coordinates |
| title_full | Angle-uniform parallel coordinates |
| title_fullStr | Angle-uniform parallel coordinates |
| title_full_unstemmed | Angle-uniform parallel coordinates |
| title_short | Angle-uniform parallel coordinates |
| title_sort | angle uniform parallel coordinates |
| topic | parallel coordinates multidimensional data deformation correlations |
| url | https://doi.org/10.1007/s41095-022-0291-7 |
| work_keys_str_mv | AT kaiyizhang angleuniformparallelcoordinates AT liangzhou angleuniformparallelcoordinates AT luchen angleuniformparallelcoordinates AT shitonghe angleuniformparallelcoordinates AT danielweiskopf angleuniformparallelcoordinates AT yunhaiwang angleuniformparallelcoordinates |