Show simple item record

The Challenge of Branch-Aware Data Manifold Exploration

dc.contributor.authorBot, Daniël M.en_US
dc.contributor.authorPeeters, Jannesen_US
dc.contributor.authorAerts, Janen_US
dc.contributor.editorGillmann, Christinaen_US
dc.contributor.editorKrone, Michaelen_US
dc.contributor.editorLenti, Simoneen_US
dc.date.accessioned2023-06-10T06:31:36Z
dc.date.available2023-06-10T06:31:36Z
dc.date.issued2023
dc.identifier.isbn978-3-03868-220-2
dc.identifier.urihttps://doi.org/10.2312/evp.20231071
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evp20231071
dc.description.abstractBranches 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.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies -> Cluster analysis; Dimensionality reduction and manifold learning
dc.subjectComputing methodologies
dc.subjectCluster analysis
dc.subjectDimensionality reduction and manifold learning
dc.titleThe Challenge of Branch-Aware Data Manifold Explorationen_US
dc.description.seriesinformationEuroVis 2023 - Posters
dc.identifier.doi10.2312/evp.20231071
dc.identifier.pages73-75
dc.identifier.pages3 pages


Files in this item

Thumbnail
Name:
073-075.pdf
Size:
693.1Kb
Format:
PDF
View/Open
Thumbnail
Name:
5939-file-i7.pdf
Size:
1.259Mb
Format:
PDF
View/Open

This item appears in the following Collection(s)

Show simple item record

Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License