Scatterplot selection is an effective approach to represent essential portions of multidimensional data in a limited display space. Various metrics for evaluation of scatterplots such as scagnostics have been presented and applied to scatterplot selection. This paper presents a new scatterplot selection technique that applies multiple metrics. The technique firstly calculates scores of scatterplots with multiple metrics and then constructs a graph by connecting similar scatterplots. The technique applies a graph coloring problem so that different colors are assigned to similar scatterplots. We can extract a set of various scatterplots by selecting them that the specific same color is assigned. This paper introduces visualization examples with a retail dataset containing multidimensional climate and sales values.

中文翻译：

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散点图选择应用图形着色问题

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。散点图评估的各种指标（例如 scagnostics）已被提出并应用于散点图选择。本文介绍了一种新的散点图选择技术，该技术适用于多个指标。该技术首先计算具有多个指标的散点图的分数，然后通过连接相似的散点图来构建图形。该技术应用图形着色问题，以便为相似的散点图分配不同的颜色。我们可以通过选择分配了特定相同颜色的散点图来提取一组各种散点图。本文介绍了包含多维气候和销售值的零售数据集的可视化示例。