Abstract Machine learning (ML) models are nowadays used in complex applications in
various domains, such as medicine, bioinformatics, and other sciences. Due to their black …

Visual analytics for machine learning has recently evolved as one of the most exciting areas
in the field of visualization. To better identify which research topics are promising and to …

Learn How to Design Effective Visualization SystemsVisualization Analysis and Design
provides a systematic, comprehensive framework for thinking about visualization in terms of …

Dimensionality Reduction (DR) is a core building block in visualizing multidimensional data.
For DR techniques to be useful in exploratory data analysis, they need to be adapted to …

Labeling data instances is an important task in machine learning and visual analytics. Both
fields provide a broad set of labeling strategies, whereby machine learning (and in particular …

Various case studies in different application domains have shown the great potential of
visual parameter space analysis to support validating and using simulation models. In order …

Visual analytics (VA) systems help data analysts solve complex problems interactively, by
integrating automated data analysis and mining, such as machine learning (ML) based …

To verify cluster separation in high-dimensional data, analysts often reduce the data with a
dimension reduction (DR) technique, and then visualize it with 2D Scatterplots, interactive …

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 …

Predictive analytics embraces an extensive range of techniques including statistical
modeling, machine learning, and data mining and is applied in business intelligence, public …