Scatterplot Selection Applying a Graph Coloring Problem

TL;DR

This paper introduces a novel scatterplot selection method that uses graph coloring to efficiently select diverse visualizations based on multiple metrics, demonstrated with a retail dataset.

Contribution

It proposes a new technique combining multiple metrics and graph coloring for diverse scatterplot selection, enhancing multidimensional data visualization.

Findings

Effective selection of diverse scatterplots demonstrated

Graph coloring ensures variety in visualizations

Applicable to multidimensional retail data

Abstract

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.