Massive simulations and arrays of sensing devices, in combination with increasing
computing resources, have generated large, complex, high-dimensional datasets used to …

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual
exploration of cluster structures of high-dimensional datasets. However, different DR …

Visualization and artificial intelligence (AI) are well-applied approaches to data analysis. On
one hand, visualization can facilitate humans in data understanding through intuitive visual …

Designing a good scatterplot can be difficult for non-experts in visualization, because they
need to decide on many parameters, such as marker size and opacity, aspect ratio, color …

Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably
reflects real-world clustering scenarios but significantly increases the complexity of …

Visual clustering is a common perceptual task in scatterplots that supports diverse analytics
tasks (eg, cluster identification). However, even with the same scatterplot, the ways of …

Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-
dimensional data. Low-dimensional representations of the data help, for instance, to explore …

A common way to evaluate the reliability of dimensionality reduction (DR) embeddings is to
quantify how well labeled classes form compact, mutually separated clusters in the …

Visual analytics has been widely studied in the past decade. One key to make visual
analytics practical for both research and industrial applications is the appropriate definition …

Scatterplots are among the most widely used visualization techniques. Compelling
scatterplot visualizations improve understanding of data by leveraging visual perception to …