Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots
Towards Understanding Human Similarity Perception in the Analysis of Large Sets of Scatter Plots
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DOI：
10.1145/2858036.2858155
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
Anshul Vikram Pandey;Josua Krause;Cristian Felix;Jeremy Boy;E. Bertini
中科院分区：
文献类型：
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作者：
Anshul Vikram Pandey;Josua Krause;Cristian Felix;Jeremy Boy;E. Bertini
我们提出了一项研究，旨在了解人类观察者在面对大量标志性的散点图表示时如何判断散点图的相似性。我们介绍的工作涉及18名具有科学背景的相似感知研究参与者。该研究要求参与者根据他们对相似度的主观感知判断，对精心挑选的一组情节进行分组，并将结果整合到一个共识相似分组中。然后，我们使用这种共识分组来产生相似性感知的见解。这项工作的主要成果是我们得出的用于描述主要感知特征的概念列表，以及这些概念如何关联和排名的描述。我们还评估了散点图诊断（散点图诊断），这是一组流行且已建立的散点图描述符，并表明它们不能可靠地再现我们参与者的判断。最后，我们讨论了本研究的主要意义以及这些结果如何用于未来的研究。
We present a study aimed at understanding how human observers judge scatter plot similarity when presented with a large set of iconic scatter plot representations. The work we present involves 18 participants with a scientific background in a similarity perception study. The study asks participants to group a carefully selected set of plots according to their subjective perceptual judgement of similarity, and it integrates the results into a consensus similarity grouping. We then use this consensus grouping to generate insights on similarity perception. The main output of this work is a list of concepts we derive to describe major perceptual features, and a description of how these concepts relate and rank. We also evaluate scagnostics (scatter plot diagnostics), a popular and established set of scatter plot descriptors, and show that they do not reliably reproduce our participants judgements. Finally, we discuss the major implications of this study and how these results can be used for future research.