Evaluating Ordering Strategies of Star Glyph Axes
This work addresses a specific visualization design issue for data analysts, but it is incremental as it builds on existing ordering strategies.
The study tackled the problem of how dimension ordering affects star glyph visualizations for clustering tasks, finding that dissimilarity-based layouts outperform similarity-based ones, particularly in cluttered settings.
Star glyphs are a well-researched visualization technique to represent multi-dimensional data. They are often used in small multiple settings for a visual comparison of many data points. However, their overall visual appearance is strongly influenced by the ordering of dimensions. To this end, two orthogonal categories of layout strategies are proposed in the literature: order dimensions by similarity to get homogeneously shaped glyphs vs. order by dissimilarity to emphasize spikes and salient shapes. While there is evidence that salient shapes support clustering tasks, evaluation, and direct comparison of data-driven ordering strategies has not received much research attention. We contribute an empirical user study to evaluate the efficiency, effectiveness, and user confidence in visual clustering tasks using star glyphs. In comparison to similarity-based ordering, our results indicate that dissimilarity-based star glyph layouts support users better in clustering tasks, especially when clutter is present.