House Donald H, Bair Alethea S, Ware Colin
Visualization Laboratory, Langford Texas A&M University, College Station 77843-3137, USA.
IEEE Trans Vis Comput Graph. 2006 Jul-Aug;12(4):509-21. doi: 10.1109/TVCG.2006.58.
This paper proposes a new experimental framework within which evidence regarding the perceptual characteristics of a visualization method can be collected, and describes how this evidence can be explored to discover principles and insights to guide the design of perceptually near-optimal visualizations. We make the case that each of the current approaches for evaluating visualizations is limited in what it can tell us about optimal tuning and visual design. We go on to argue that our new approach is better suited to optimizing the kinds of complex visual displays that are commonly created in visualization. Our method uses human-in-the-loop experiments to selectively search through the parameter space of a visualization method, generating large databases of rated visualization solutions. Data mining is then used to extract results from the database, ranging from highly specific exemplar visualizations for a particular data set, to more broadly applicable guidelines for visualization design. We illustrate our approach using a recent study of optimal texturing for layered surfaces viewed in stereo and in motion. We show that a genetic algorithm is a valuable way of guiding the human-in-the-loop search through visualization parameter space. We also demonstrate several useful data mining methods including clustering, principal component analysis, neural networks, and statistical comparisons of functions of parameters.
本文提出了一个新的实验框架，在该框架内可以收集有关可视化方法感知特征的证据，并描述如何探索这些证据以发现原则和见解，从而指导感知上接近最优的可视化设计。我们认为，当前评估可视化的每种方法在告诉我们关于最优调整和视觉设计方面都存在局限性。我们接着论证，我们的新方法更适合优化可视化中常见的复杂视觉显示。我们的方法使用人在回路实验来有选择地搜索可视化方法的参数空间，生成大量经过评级的可视化解决方案数据库。然后使用数据挖掘从数据库中提取结果，范围从针对特定数据集的高度特定的示例可视化，到更广泛适用的可视化设计指南。我们通过最近一项关于立体和动态观察的分层表面的最优纹理研究来说明我们的方法。我们表明，遗传算法是指导人在回路搜索可视化参数空间的一种有价值的方法。我们还展示了几种有用的数据挖掘方法，包括聚类、主成分分析、神经网络以及参数函数的统计比较。
