The Challenge of Branch-Aware Data Manifold Exploration

Bot, Daniël M.Peeters, JannesAerts, JanGillmann, ChristinaKrone, MichaelLenti, Simone2023-06-102023-06-102023978-3-03868-220-2https://doi.org/10.2312/evp.20231071https://diglib.eg.org:443/handle/10.2312/evp20231071Branches 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.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies -> Cluster analysis; Dimensionality reduction and manifold learningComputing methodologiesCluster analysisDimensionality reduction and manifold learningThe Challenge of Branch-Aware Data Manifold Exploration10.2312/evp.2023107173-753 pages