In this paper, we present a systematization of techniques that use quality metrics to help in
the visual exploration of meaningful patterns in high-dimensional data. In a number of recent …

Dimensionality reduction methods, also known as projections, are frequently used in
multidimensional data exploration in machine learning, data science, and information …

Traditional scatterplots fail to scale as the complexity and amount of data increases. In
response, there exist many design options that modify or expand the traditional scatterplot …

The visualization community has developed to date many intuitions and understandings of
how to judge the quality of views in visualizing data. The computation of a visualization's …

We provide two contributions, a taxonomy of visual cluster separation factors in scatterplots,
and an in‐depth qualitative evaluation of two recently proposed and validated separation …

In this paper, we explore how the capacity limits of attention influence the effectiveness of
information visualizations. We conducted a series of experiments to test how visual feature …

Visual quality measures seek to algorithmically imitate human judgments of patterns such as
class separability, correlation, or outliers. In this paper, we propose a novel data‐driven …

Data features and class probabilities are two main perspectives when, eg, evaluating model
results and identifying problematic items. Class probabilities represent the likelihood that …

High‐dimensional data sets are a prevalent occurrence in many application domains. This
data is commonly visualized using dimensionality reduction (DR) methods. DR methods …

Our goal is to accurately model human class separation judgements in color-coded
scatterplots. Towards this goal, we propose a set of 2002 visual separation measures, by …