Transforming Scagnostics to Reveal Hidden Features

November 9th, 2014

Categories: Visualization

Inspecting the Outlying measure on the Sleep data: The selected variables are body weight vs. brain weight
Inspecting the Outlying measure on the Sleep data: The selected variables are body weight vs. brain weight

Authors

Dang, T., Wilkinson, L.

About

Scagnostics (Scatterplot Diagnostics) were developed by Wilkinson et al., based on an idea of Paul and John Tukey, in order to discern meaningful patterns in large collections of scatterplots. The Tukeys’ original idea was intended to overcome the impediments involved in examining large scatterplot matrices (multiplicity of plots and lack of detail). Wilkinson’s implementation enabled for the first time scagnostics computations on many points as well as many plots. Unfortunately, scagnostics are sensitive to scale transformations. We illustrate the extent of this sensitivity and show how it is possible to pair statistical transformations with scagnostics to enable discovery of hidden structures in data that are not discernible in untransformed visualizations.

Keywords: Scagnostics, Scatterplot matrix, Transformation, High-Dimensional Visual Analytics

Resources

PDF

Citation

Dang, T., Wilkinson, L., Transforming Scagnostics to Reveal Hidden Features, Proceedings of the IEEE Vis 2014 (Visual Analytics Science and Technology - VAST Conference), Paris, France, November 9th, 2014.