Thorsøe, RasmusLocher, PeterRathish, HarithSchulz, Hans-JörgLinsen, LarsThies, Justus2024-09-092024-09-092024978-3-03868-247-9https://doi.org/10.2312/vmv.20241198https://diglib.eg.org/handle/10.2312/vmv20241198Axis breaks are used in charts, for example, to reduce whitespace, to accommodate outliers, or to show data at different scales. Proposed in the 1980s, axis breaks have not gotten much attention since then in terms of what characterizes ''good'' breaks, how many of them to introduce, and where to best place them? To answer these questions, we propose a five-step framework that specifies (1) the number of breaks, (2) their position, (3) the scaling of the resulting subaxes, (4) the ''niceness'' of the breaks, and (5) the formatting of the breaks. To apply this framework, we introduce a new metric, called skew, to quantify how unevenly distributed points are along an axis. Skew is then used as a cost function to formulate the search for optimal axis breaks as a clustering problem, which we solve by applying a dynamic k-means algorithm. We apply our framework specifically to Parallel Coordinate Plots and compare our algorithmic solution to established methods like percentile breaks and Jenks natural breaks. An interactive testbed to try our framework as well as its source code are made freely available.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visualization techniques; Information visualizationHuman centered computing → Visualization techniquesInformation visualization10.2312/vmv.202411988 pages