All Outputs (2)

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.

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.