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Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates
Author(s) -
Blumenschein Michael,
Zhang Xuan,
Pomerenke David,
Keim Daniel A.,
Fuchs Johannes
Publication year - 2020
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.14000
Subject(s) - computer science , identification (biology) , dimension (graph theory) , task (project management) , clutter , focus (optics) , cluster (spacecraft) , artificial intelligence , data mining , quality (philosophy) , machine learning , pattern recognition (psychology) , mathematics , telecommunications , radar , philosophy , botany , physics , management , epistemology , pure mathematics , optics , economics , biology , programming language
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.