Automatic Scatterplot Design Optimization for Clustering Identification

TL;DR

This paper introduces an automatic optimization tool for scatterplot design that enhances clustering visibility by adjusting visual parameters, validated through user studies to improve data understanding.

Contribution

It presents a novel framework using merge trees to automatically optimize scatterplot visual encodings for better cluster detection.

Findings

Efficiently generates high-quality scatterplots from large parameter spaces.

Improves clustering visibility in scatterplots.

Validated with user and case studies.

Abstract

Scatterplots are among the most widely used visualization techniques. Compelling scatterplot visualizations improve understanding of data by leveraging visual perception to boost awareness when performing specific visual analytic tasks. Design choices in scatterplots, such as graphical encodings or data aspects, can directly impact decision-making quality for low-level tasks like clustering. Hence, constructing frameworks that consider both the perceptions of the visual encodings and the task being performed enables optimizing visualizations to maximize efficacy. In this paper, we propose an automatic tool to optimize the design factors of scatterplots to reveal the most salient cluster structure. Our approach leverages the merge tree data structure to identify the clusters and optimize the choice of subsampling algorithm, sampling rate, marker size, and marker opacity used to generate a scatterplot image. We validate our approach with user and case studies that show it efficiently provides high-quality scatterplot designs from a large parameter space.