Integral Curve Clustering and Simplification for Flow Visualization: A Comparative Evaluation
Integral Curve Clustering and Simplification for Flow Visualization: A Comparative Evaluation
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DOI：
10.1109/tvcg.2019.2940935
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发表时间：
2019-09
影响因子：
5.2
通讯作者：
Lieyu Shi;Robert S. Laramee;Guoning Chen
中科院分区：
文献类型：
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作者：
Lieyu Shi;Robert S. Laramee;Guoning Chen
无监督聚类技术已被广泛应用于流动模拟数据中，以减轻结果可视化中的杂波和遮挡。然而，缺乏系统的指导方针，以供用户评估（定量和视觉）适当的聚类技术和流线和路径曲线的相似性措施。在这项工作中，我们提供了一些流行的曲线聚类技术的概述。然后，我们进行了一项全面的实验研究，以定性和定量地比较这些聚类技术以及流可视化文献中使用的流行相似性度量。基于我们的实验结果，我们得出了根据可视化任务的要求选择合适的聚类技术和相似性度量的经验准则。我们相信我们的工作将为大规模流量数据生成有意义的简化表示的任务提供信息，并激发对更精细的聚类技术选择指导的持续研究。
Unsupervised clustering techniques have been widely applied to flow simulation data to alleviate clutter and occlusion in the resulting visualization. However, there is an absence of systematic guidelines for users to evaluate (both quantitatively and visually) the appropriate clustering technique and similarity measures for streamline and pathline curves. In this work, we provide an overview of a number of prevailing curve clustering techniques. We then perform a comprehensive experimental study to qualitatively and quantitatively compare these clustering techniques coupled with popular similarity measures used in the flow visualization literature. Based on our experimental results, we derive empirical guidelines for selecting the appropriate clustering technique and similarity measure given the requirements of the visualization task. We believe our work will inform the task of generating meaningful reduced representations for large-scale flow data and inspire the continuous investigation of a more refined guidance on clustering technique selection.