论文标题
散点图选择应用图形问题
Scatterplot Selection Applying a Graph Coloring Problem
论文作者
论文摘要
散点图选择是在有限的显示空间中代表多维数据基本部分的有效方法。已经提出了用于评估散点图（例如Scagnostics）的各种指标，并应用于散点图选择。本文提出了一种应用多个指标的新散点图选择技术。该技术首先通过多个指标来计算散点图的得分，然后通过连接相似的散点图来构造图形。该技术应用了图形问题，因此将不同的颜色分配给相似的散点图。我们可以通过选择分配特定的相同颜色来提取一组各种散点图。本文介绍了包含多维气候和销售价值的零售数据集的可视化示例。
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.
