Scatterplot Selection Applying a Graph Coloring Problem
This work addresses the challenge of visualizing high-dimensional data in limited space for data analysts, but it is incremental as it builds on existing metrics like scagnostics.
The paper tackles the problem of selecting diverse scatterplots from multidimensional data by applying a graph coloring approach to ensure dissimilarity among chosen plots, and demonstrates the technique on a retail dataset with climate and sales values.
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.