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UnTangle Map: Visual Analysis of Probabilistic Multi-Label Data

机译：解缠贴图：概率多标签数据的可视化分析

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摘要

Data with multiple probabilistic labels are common in many situations. For example, a movie may be associated with multiple genres with different levels of confidence. Despite their ubiquity, the problem of visualizing probabilistic labels has not been adequately addressed. Existing approaches often either discard the probabilistic information, or map the data to a low-dimensional subspace where their associations with original labels are obscured. In this paper, we propose a novel visual technique, , for visualizing probabilistic multi-labels. In our proposed visualization, data items are placed inside a web of connected triangles, with labels assigned to the triangle vertices such that nearby labels are more relevant to each other. The positions of the data items are determined based on the probabilistic associations between items and labels. UnTangle Map provides both (a) an automatic label placement algorithm, and (b) adaptive interactions that allow users to control the label positioning for different information needs. Our work makes a unique contribution by providing an effective way to investigate the relationship between data items and their probabilistic labels, as well as the relationships among labels. Our user study suggests that the visualization effectively helps users discover emergent patterns and compare the nuances of probabilistic information in the data labels.
机译：具有多个概率标签的数据在许多情况下很常见。例如，电影可以以不同的置信度与多种类型相关联。尽管它们无处不在，但仍未充分解决可视化概率标签的问题。现有方法通常要么丢弃概率信息，要么将数据映射到低维子空间，在该空间中它们与原始标签的关联被遮盖了。在本文中，我们提出了一种新颖的视觉技术，用于可视化概率多标签。在我们提出的可视化中，将数据项放置在连接的三角形网中，并为三角形的顶点分配了标签，以使附近的标签彼此之间更加相关。基于项目与标签之间的概率关联来确定数据项目的位置。 UnTangle Map提供了（a）自动标签放置算法和（b）自适应交互功能，这些功能允许用户控制标签位置以适应不同的信息需求。我们的工作通过提供一种有效的方法来研究数据项与其概率标签之间的关系以及标签之间的关系，从而做出了独特的贡献。我们的用户研究表明，可视化可以有效地帮助用户发现紧急情况的模式并比较数据标签中概率信息的细微差别。

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