Multidimensional data visualization applying a variety-oriented scatterplot selection technique
Multidimensional data visualization applying a variety-oriented scatterplot selection technique
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DOI：
10.1007/s12650-022-00871-6
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发表时间：
2022-08
影响因子：
1.7
通讯作者：
T. Itoh;Asuka Nakabayashi;Mariko Hagita
中科院分区：
文献类型：
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作者：
T. Itoh;Asuka Nakabayashi;Mariko Hagita
多维数据可视化是信息可视化中最活跃的研究课题之一，因为我们日常生活中的各种信息形成多维数据集。散点图选择是在有限的显示空间中表示多维数据的基本部分的有效方法。用于评估散点图的各种指标（例如诊断学）已应用于散点图选择。本研究课题的未解决问题之一是，如果我们仅应用其中一种指标，则无法选择各种散点图。换句话说，当我们想要选择多种散点图时，我们可能希望以平衡的方式同时应用多个指标。本文提出了一种新的散点图选择技术来解决这个问题。首先，该技术计算具有多个指标的散点图的分数，然后通过连接具有相似分数的散点图对来构建图表。接下来，它使用图形着色算法为具有相似分数的散点图分配不同的颜色。我们可以通过选择指定了特定相同颜色的散点图来提取一组各种散点图。本文介绍了两个案例研究：前一个研究是零售交易数据集，后一个研究是设计优化数据集。图解摘要
Multidimensional data visualization is one of the most active research topics in information visualization since various information in our daily life forms multidimensional datasets. Scatterplot selection is an effective approach to represent essential portions of multidimensional data in a limited display space. Various metrics for evaluating scatterplots, such as scagnostics, have been applied to scatterplot selection. One of the open problems of this research topic is that various scatterplots cannot be selected if we simply apply one of the metrics. In other words, we may want to apply multiple metrics simultaneously in a balanced manner when we want to select a variety of scatterplots. This paper presents a new scatterplot selection technique that solves this problem. First, the technique calculates the scores of scatterplots with multiple metrics and then constructs a graph by connecting pairs of scatterplots that have similar scores. Next, it uses a graph coloring algorithm to assign different colors to scatterplots that have similar scores. We can extract a set of various scatterplots by selecting them that the specific same color is assigned. This paper introduces two case studies: the former study is with a retail transaction dataset while the latter study is with a design optimization dataset.Graphical abstract