Integral Curve Clustering and Simplification for Flow Visualization: A Comparative Evaluation
Unsupervised clustering techniques have been widely applied to flow simulation data to alleviate clutter and occlusion in theresulting visualization. However, there is an absence of systematic guidelines for users to evaluate (both quantitatively and visually) theappropriate clustering technique and similarity measures for streamline and pathline curves. In this work, we provide an overview of anumber of prevailing curve clustering techniques. We then perform a comprehensive experimental study to qualitatively and quantitativelycompare these clustering techniques coupled with popular similarity measures used in the flow visualization literature. Based on ourexperimental results, we derive empirical guidelines for selecting the appropriate clustering technique and similarity measure given therequirements of the visualization task. We believe our work will inform the task of generating meaningful reduced representations forlarge-scale flow data and inspire the continuous investigation of a more refined guidance on clustering technique selection.