All Outputs (4)

MLCut: exploring multi-level cuts in dendrograms for biological data (2016)
Presentation / Conference Contribution
Vogogias, A., Kennedy, J., Archambault, D., Anne Smith, V., & Currant, H. (2016, September). MLCut: exploring multi-level cuts in dendrograms for biological data. Presented at Computer Graphics & Visual Computing (CGVC) 2016

Choosing a single similarity threshold for cutting dendrograms is not sufficient for performing hierarchical clustering analysis of heterogeneous data sets. In addition, alternative automated or semi-automated methods that cut dendrograms in multiple... Read More about MLCut: exploring multi-level cuts in dendrograms for biological data.

Telling stories about dynamic networks with graph comics. (2016)
Presentation / Conference Contribution
Bach, B., Kerracher, N., Hall, K. W., Carpendale, S., Kennedy, J., & Henry Riche, N. (2016, May). Telling stories about dynamic networks with graph comics. Presented at CHI 16

In this paper, we explore graph comics as a medium to communicate
changes in dynamic networks. While previous research
has focused on visualizing dynamic networks for data
exploration, we want to see if we can take advantage of the
visual express... Read More about Telling stories about dynamic networks with graph comics..

Large-scale Argument Visualization (LSAV) (2016)
Presentation / Conference Contribution
Khartabil, D., Wells, S., & Kennedy, J. (2016, June). Large-scale Argument Visualization (LSAV). Presented at 18th EG/VGTC Conference on Visualization

Arguments are structures of premises and conclusions that underpin rational reasoning processes. Within complex knowledge domains, especially if they are contentious, argument structures can become large and complex. Visualization tools have been dev... Read More about Large-scale Argument Visualization (LSAV).

Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data (2016)
Presentation / Conference Contribution
Vogogias, A., Kennedy, J., & Archambault, D. (2016, July). Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data. Presented at Eurovis 2016

Rigid adherence to pre-specified thresholds and static graphical representations can lead to incorrect decisions on merging of clusters. As an alternative to existing automated or semi-automated methods, we developed a visual analytics approach for p... Read More about Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data.