Dynamic Color Assignment for Hierarchical Data

TL;DR

This paper introduces a dynamic color assignment method for hierarchical data visualization that optimizes discriminability, harmony, and spatial distribution, improving clarity and consistency across hierarchical levels.

Contribution

It proposes a multi-objective optimization approach that dynamically assigns colors in hierarchical data, addressing limitations of existing methods and enhancing visual clarity.

Findings

Effective in generating high-quality color assignments

Improves visual clarity and hierarchical consistency

Validated through experiments and user study

Abstract

Assigning discriminable and harmonic colors to samples according to their class labels and spatial distribution can generate attractive visualizations and facilitate data exploration. However, as the number of classes increases, it is challenging to generate a high-quality color assignment result that accommodates all classes simultaneously. A practical solution is to organize classes into a hierarchy and then dynamically assign colors during exploration. However, existing color assignment methods fall short in generating high-quality color assignment results and dynamically aligning them with hierarchical structures. To address this issue, we develop a dynamic color assignment method for hierarchical data, which is formulated as a multi-objective optimization problem. This method simultaneously considers color discriminability, color harmony, and spatial distribution at each hierarchical level. By using the colors of parent classes to guide the color assignment of their child classes, our method further promotes both consistency and clarity across hierarchical levels. We demonstrate the effectiveness of our method in generating dynamic color assignment results with quantitative experiments and a user study.