Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates
Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates
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DOI：
10.1111/cgf.14000
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发表时间：
2020-06
影响因子：
2.5
通讯作者：
M. Blumenschein;Xuan Zhang;David Pomerenke;D. Keim;Johannes Fuchs
中科院分区：
文献类型：
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作者：
M. Blumenschein;Xuan Zhang;David Pomerenke;D. Keim;Johannes Fuchs
在平行坐标图（PCP）中感知模式的能力受到维度排序的严重影响。虽然社区已经提出了30多个自动排序策略，但我们仍然缺乏为给定任务选择适当策略的经验指导。在本文中，我们首先提出了一个分类的任务和模式，并分析PCP重新排序策略有助于检测它们。基于我们的分类，我们进行了一个经验性的用户研究，31名参与者评估集群识别任务的重新排序策略。我们特别使用合成和真实的世界数据集来测量两种不同策略的时间、识别质量和用户信心。我们的研究结果表明，有些出乎意料的是，参与者往往专注于不同的，而不是相似的维度对检测集群时，并在他们的答案更有信心。当增加数据中的杂乱数量时，这一点尤其如此。由于这些发现，我们提出了一个新的重排序策略的基础上相邻的维度对的相异性。
The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users’ confidence for two different strategies using both synthetic and real‐world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.