Designers often create visualizations to achieve specific high-level analytical or
communication goals. These goals require people to naturally extract complex …

Large language models (LLMs) have facilitated significant strides in generating
conversational agents, enabling seamless, contextually relevant dialogues across diverse …

Existing guidelines for categorical color selection are heuristic, often grounded in intuition
rather than empirical studies of readers' abilities. While design conventions recommend …

Shape is commonly used to distinguish between categories in multi-class scatterplots.
However, existing guidelines for choosing effective shape palettes rely largely on intuition …

Visualization is crucial to augment and enhance human understanding and decision-making
in today's data-driven world. However, the way data are visualized can influence and …

Cluster separation is a task typically tackled by widely used clustering techniques, such as k‐
means or DBSCAN. However, these algorithms are based on non‐perceptual metrics, and …

The semantic similarity between documents of a text corpus can be visualized using map-
like metaphors based on two-dimensional scatterplot layouts. These layouts result from a …

Data visualizations help extract insights from datasets, but reaching these insights requires
decomposing high level goals into low-level analytic tasks that can be complex due to …

Clustering is an essential technique across various domains, such as data science, machine
learning, and explainable artificial intelligence. Information visualization and visual analytics …

Scatterplots are frequently shared across different displays in collaborative and
communicative visual analytics. However, variations in displays diversify scatterplot sizes …