Visual-interactive Exploration of Interesting Multivariate Relations in Mixed Research Data Sets

Simple item page
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.authorSteiger, Martinen_US
dc.contributor.authorWidmer, Svenen_US
dc.contributor.authorLücke-Tieke, Hendriken_US
dc.contributor.authorMay, Thorstenen_US
dc.contributor.authorKohlhammer, Jörnen_US
dc.contributor.editorH. Carr, P. Rheingans, and H. Schumannen_US
dc.date.accessioned2015-03-03T12:35:31Z
dc.date.available2015-03-03T12:35:31Z
dc.date.issued2014en_US
dc.description.abstractThe analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12385en_US
dc.publisherThe Eurographics Association and John Wiley and Sons Ltd.en_US
dc.titleVisual-interactive Exploration of Interesting Multivariate Relations in Mixed Research Data Setsen_US
Files
Collections
33-Issue 3
EuroVis14: Eurographics Conference on Visualization