PC-Expo：一种基于度量的交互式平行坐标轴重排方法
PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel Coordinate Displays
摘要：
PCP中的轴排序根据用户对PCP多段线的感知从数据中呈现特定的故事。现有的工作集中在直接优化PCP轴排序的基础上，一些常见的分析任务，如聚类，邻域和相关性。然而，基于这些公共属性的PCP轴的直接优化是限制性的，因为它不考虑轴之间发生的多个属性，以及数据中小区域中发生的局部属性。此外，许多这些技术不支持人在回路（HIL）的范例，这是至关重要的（i）可解释性和（ii）在没有单一的重新排序方案适合用户目标的情况下。为了缓解这些问题，我们提出了PC博览会，一个实时的可视化分析框架，所有在一个PCP线模式检测，和轴重新排序。我们研究了不同数据分析任务和数据集的PCP中的线模式的连接。PC-Expo通过为12个最常见的分析任务（属性）开发实时、本地检测方案，扩展了之前在PCP轴重新排序方面的工作。用户可以通过直接优化他们选择的属性来选择他们想要与PCP一起呈现的故事。这些属性可以进行排名，或使用单独的权重进行组合，为轴重新排序创建自定义优化方案。用户可以控制他们想要在数据中使用其检测方案的粒度，从而允许探索局部区域。PC-Expo还支持HIL轴通过局部属性可视化重新排序，显示每个轴对的颗粒活动区域。当没有一个重新排序方案符合用户目标时，局部属性可视化有助于基于多个属性的PCP轴重新排序。
英文摘要：
The axes ordering in PCP presents a particular story from the data based on the user perception of PCP polylines. Existing works focus on directly optimizing for PCP axes ordering based on some common analysis tasks like clustering, neighborhood, and correlation. However, direct optimization for PCP axes based on these common properties is restrictive because it does not account for multiple properties occurring between the axes, and for local properties that occur in small regions in the data. Also, many of these techniques do not support the human-in-the-loop (HIL) paradigm, which is crucial (i) for explainability and (ii) in cases where no single reordering scheme fits the user goals. To alleviate these problems, we present PC-Expo, a real-time visual analytics framework for all-in-one PCP line pattern detection, and axes reordering. We studied the connection of line patterns in PCPs with different data analysis tasks and datasets. PC-Expo expands prior work on PCP axes reordering by developing real-time, local detection schemes for the 12 most common analysis tasks (properties). Users can choose the story they want to present with PCPs by optimizing directly over their choice of properties. These properties can be ranked, or combined using individual weights, creating a custom optimization scheme for axes reordering. Users can control the granularity at which they want to work with their detection scheme in the data, allowing exploration of local regions. PC-Expo also supports HIL axes reordering via local-property visualization, which shows the regions of granular activity for every axis pair. Local-property visualization is helpful for PCP axes reordering based on multiple properties, when no single reordering scheme fits the user goals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
