论文标题
通过添加冗余的视觉信息来查找可视化数据中的模式
Finding Patterns in Visualized Data by Adding Redundant Visual Information
论文作者
论文摘要
我们提出“ PATRED”，该技术使用添加冗余信息来促进在图表的视觉探索期间在线路上检测特定的，通常描述的模式。我们使用九个距离指标（例如Euclidean，Pearson，Mutual Information和Jaccard）和数据科学家的判断，将这种技术的不同版本在添加冗余的方式上有所不同。通过相关性（R2），F1评分和数据科学家的平均排名分析结果。一些距离指标始终从添加冗余信息中受益，而另一些距离指标仅对特定类型的数据扰动有所增强。结果证明了添加冗余以改善视觉探索过程中时间序列数据模式的识别的价值。
We present "PATRED", a technique that uses the addition of redundant information to facilitate the detection of specific, generally described patterns in line-charts during the visual exploration of the charts. We compared different versions of this technique, that differed in the way redundancy was added, using nine distance metrics (such as Euclidean, Pearson, Mutual Information and Jaccard) with judgments from data scientists which served as the "ground truth". Results were analyzed with correlations (R2), F1 scores and Mutual Information with the average ranking by the data scientists. Some distance metrics consistently benefit from the addition of redundant information, while others are only enhanced for specific types of data perturbations. The results demonstrate the value of adding redundancy to improve the identification of patterns in time-series data during visual exploration.
