The Challenge of Branch-Aware Data Manifold Exploration
| dc.contributor.author | Bot, Daniƫl M. | en_US |
| dc.contributor.author | Peeters, Jannes | en_US |
| dc.contributor.author | Aerts, Jan | en_US |
| dc.contributor.editor | Gillmann, Christina | en_US |
| dc.contributor.editor | Krone, Michael | en_US |
| dc.contributor.editor | Lenti, Simone | en_US |
| dc.date.accessioned | 2023-06-10T06:31:36Z | |
| dc.date.available | 2023-06-10T06:31:36Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Branches within clusters can represent meaningful subgroups that should be explored. In general, automatically detecting branching structures within clusters requires analysing the distances between data points and a centrality metric, resulting in a complex two-dimensional hierarchy. This poster describes abstractions for this data and formulates requirements for a visualisation, building towards a comprehensive branch-aware cluster exploration interface. | en_US |
| dc.description.seriesinformation | EuroVis 2023 - Posters | |
| dc.identifier.doi | 10.2312/evp.20231071 | |
| dc.identifier.isbn | 978-3-03868-220-2 | |
| dc.identifier.pages | 73-75 | |
| dc.identifier.pages | 3 pages | |
| dc.identifier.uri | https://doi.org/10.2312/evp.20231071 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/evp20231071 | |
| dc.publisher | The Eurographics Association | en_US |
| dc.rights | Attribution 4.0 International License | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | CCS Concepts: Computing methodologies -> Cluster analysis; Dimensionality reduction and manifold learning | |
| dc.subject | Computing methodologies | |
| dc.subject | Cluster analysis | |
| dc.subject | Dimensionality reduction and manifold learning | |
| dc.title | The Challenge of Branch-Aware Data Manifold Exploration | en_US |