Viewpoint-Based Quality for Analyzing and Exploring 3D Multidimensional Projections

While 2D projections are established tools for exploring high-dimensional data, the effectiveness of their 3D counterparts is still a matter of debate. In this work, we address this from a multifaceted quality perspective. We first propose a viewpoint-dependent definition of 3D projection quality and show how this captures the visual variability in 3D projections much better than aggregated, single-value, quality metrics. Next, we propose an interactive exploration tool for finding high-quality viewpoints for 3D projections. We use our tool in an user evaluation to gauge how our quality metric correlates with user-perceived quality for a cluster identification task. Our results show that our metric can predict well viewpoints deemed good by users and that our tool increases the users’ preference for 3D projections as compared to classical 2D projections.