The Challenge of Branch-Aware Data Manifold Exploration
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.
BibTeX
@inproceedings {10.2312:evp.20231071,
booktitle = {EuroVis 2023 - Posters},
editor = {Gillmann, Christina and Krone, Michael and Lenti, Simone},
title = {{The Challenge of Branch-Aware Data Manifold Exploration}},
author = {Bot, Daniël M. and Peeters, Jannes and Aerts, Jan},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-220-2},
DOI = {10.2312/evp.20231071}
}
booktitle = {EuroVis 2023 - Posters},
editor = {Gillmann, Christina and Krone, Michael and Lenti, Simone},
title = {{The Challenge of Branch-Aware Data Manifold Exploration}},
author = {Bot, Daniël M. and Peeters, Jannes and Aerts, Jan},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-220-2},
DOI = {10.2312/evp.20231071}
}