S
Sebastian Bremm
Researcher at Technische Universität Darmstadt
Publications - 28
Citations - 836
Sebastian Bremm is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Visual analytics & Cluster analysis. The author has an hindex of 17, co-authored 27 publications receiving 772 citations.
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Journal ArticleDOI
Space-in-time and time-in-space self-organizing maps for exploring spatiotemporal patterns
Gennady Andrienko,Natalia Andrienko,Sebastian Bremm,Tobias Schreck,T. von Landesberger,Peter Bak,Daniel A. Keim +6 more
TL;DR: The use of a framework based on the “Self‐Organizing Map” (SOM) method combined with a set of interactive visual tools supporting both analytic perspectives for discovery of previously unknown patterns in 41‐years time series of 7 crime rate attributes in the states of the USA is described.
Proceedings ArticleDOI
Interactive visual comparison of multiple trees
Sebastian Bremm,Tatiana von Landesberger,Martin Hess,Tobias Schreck,Philipp Weil,Kay Hamacherk +5 more
TL;DR: A novel visual analytics approach for the comparison of multiple hierarchies focusing on both global and local structures is introduced, and is applied to a phylogenetic data set on bacterial ancestry, demonstrating its application benefit.
Journal ArticleDOI
Techniques for precision-based visual analysis of projected data
TL;DR: This work addresses the visual assessment of projection precision by an approach integrating an appropriately designed projection precision measure directly into the projection visualization and shows how the interactive precision quality visualization system helps to examine the preservation of original data properties in projected space.
Proceedings ArticleDOI
Visual analytics methods for categoric spatio-temporal data
TL;DR: A new approach which interactively combines visualization of categorical changes over time; various spatial data displays; computational techniques for task-oriented selection of time steps provides an expressive visualization with regard to either the overall evolution over time or unusual changes.
Journal ArticleDOI
Assisted descriptor selection based on visual comparative data analysis
TL;DR: A novel system for data description selection, which facilitates the user's access to the data analysis process and supports the comparison of data descriptors with differing dimensionality for unlabeled data.