All Outputs (4)

MLCut: exploring multi-level cuts in dendrograms for biological data (2016)
Conference Proceeding
Vogogias, A., Kennedy, J., Archambault, D., Anne Smith, V., & Currant, H. (2016). MLCut: exploring multi-level cuts in dendrograms for biological data. In C. Turkay, & T. Ruan Wan (Eds.), Computer Graphics and Visual Computing (CGVC). https://doi.org/10.2312/cgvc.20161288

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)
Conference Proceeding
Bach, B., Kerracher, N., Hall, K. W., Carpendale, S., Kennedy, J., & Henry Riche, N. (2016). Telling stories about dynamic networks with graph comics. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI'16) (1-13). https://doi.org/10.1145/2858036.2858387

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)
Conference Proceeding
Khartabil, D., Wells, S., & Kennedy, J. (2016). Large-scale Argument Visualization (LSAV). In Eurographics Conference on Visualization (EuroVis), Posters Track (2016)

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)
Conference Proceeding
Vogogias, A., Kennedy, J., & Archambault, D. (2016). Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data. In T. Isenberg, & F. Sadlo (Eds.), EuroVis 2016 - Posters (1-3). https://doi.org/10.2312/eurp.20161127

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.