Cluster-based visualization for merger tree data: The challenge of missing expectations

Annie Preston, Kwan Liu Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Scientific simulations are yielding increasing amounts of data; to visualize the full output from a simulation, one must first reduce clutter and obstruction. Clustering algorithms are common tools for condensing information and decreasing clutter when analyzing and visualizing simulation output. Often, simulation data have intuitive groupings. In some cases, though, such as merger trees from N-body dark matter simulations, there are limited expectations for clustering results. We investigate cluster-based visualization design for merger tree data, testing whether multidimensional encodings and opening the "black box" can allow for meaningful representation and exploration of these data.

Original languageEnglish (US)
Title of host publication2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings
EditorsBerk Geveci, Gordon Kindlmann, Luis Gustavo Nonato
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages42-46
Number of pages5
ISBN (Electronic)9781538668825
DOIs
StatePublished - Oct 2018
Event2018 IEEE Scientific Visualization Conference, SciVis 2018 - Berlin, Germany
Duration: Oct 21 2018Oct 26 2018

Publication series

Name2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings

Conference

Conference2018 IEEE Scientific Visualization Conference, SciVis 2018
CountryGermany
CityBerlin
Period10/21/1810/26/18

Keywords

  • Clustering
  • Cosmology
  • Large data visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Media Technology

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    Preston, A., & Ma, K. L. (2018). Cluster-based visualization for merger tree data: The challenge of missing expectations. In B. Geveci, G. Kindlmann, & L. G. Nonato (Eds.), 2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings (pp. 42-46). [8823586] (2018 IEEE Scientific Visualization Conference, SciVis 2018 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SciVis.2018.8823586