Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates

Computer Graphics Forum, vol. 39, pp. 537–549

Abstract

Abstract The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.

Authors 5

1. Michael Blumenschein corresponding

   University of Konstanz

   Affiliation as printed

   University of Konstanz Germany

   University of Konstanz, Germany

2. Xuan Zhang Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University Germany

   RWTH Aachen University, Germany

3. David Pomerenke

   University of Konstanz

   Affiliation as printed

   University of Konstanz Germany

   University of Konstanz, Germany

4. Daniel A. Keim

   University of Konstanz

   Affiliation as printed

   University of Konstanz Germany

   University of Konstanz, Germany

5. Johannes Fuchs

   University of Konstanz

   Affiliation as printed

   University of Konstanz Germany

   University of Konstanz, Germany