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Evaluation of Multivariate Visualization on a Multivariate Task

机译:多元任务的多元可视化评估

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Multivariate visualization techniques have attracted great interest as the dimensionality of data sets grows. One premise of such techniques is that simultaneous visual representation of multiple variables will enable the data analyst to detect patterns amongst multiple variables. Such insights could lead to development of new techniques for rigorous (numerical) analysis of complex relationships hidden within the data. Two natural questions arise from this premise: Which multivariate visualization techniques are the most effective for high-dimensional data sets? How does the analysis task change this utility ranking? We present a user study with a new task to answer the first question. We provide some insights to the second question based on the results of our study and results available in the literature. Our task led to significant differences in error, response time, and subjective workload ratings amongst four visualization techniques. We implemented three integrated techniques (Data-driven Spots, Oriented Slivers, and Attribute Blocks), as well as a baseline case of separate grayscale images. The baseline case fared poorly on all three measures, whereas Datadriven Spots yielded the best accuracy and was among the best in response time. These results differ from comparisons of similar techniques with other tasks, and we review all the techniques, tasks, and results (from our work and previous work) to understand the reasons for this discrepancy.
机译:随着数据集维数的增长,多元可视化技术引起了极大的兴趣。这种技术的一个前提是多个变量的同时可视化表示将使数据分析人员能够检测多个变量之间的模式。这些见解可能会导致开发新技术,以对隐藏在数据中的复杂关系进行严格(数字)分析。从这个前提产生两个自然的问题:哪种多维可视化技术对于高维数据集最有效?分析任务如何改变该实用程序排名?我们为用户研究提出了一项新任务,以回答第一个问题。根据我们的研究结果和文献中提供的结果,我们对第二个问题提供了一些见解。我们的任务在四种可视化技术之间导致了错误,响应时间和主观工作负荷等级的显着差异。我们实施了三种集成技术(数据驱动斑点,定向条和属性块),以及单独的灰度图像的基准情况。基线情况在所有三个指标上均表现不佳,而数据驱动型竞标点的准确度最高,并且在响应时间上也处于最佳状态。这些结果不同于类似技术与其他任务的比较,并且我们查看所有技术,任务和结果(来自我们的工作和先前的工作)以了解出现这种差异的原因。

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