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 …

As more people rely on visualization to inform their personal and collective decisions,
researchers have focused on a broader range of audiences, including “novices.” But …

Multi-class classifiers often compute scores for the classification samples describing
probabilities to belong to different classes. In order to improve the performance of such …

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

A myriad of design rules for what constitutes a “good” colormap can be found in the
literature. Some common rules include order, uniformity, and high discriminative power …

In explorative data analysis, the data under consideration often resides in a high-
dimensional (HD) data space. Currently many methods are available to analyze this type of …

Finding interesting patterns in large scatter plot spaces is a challenging problem and
becomes even more difficult with increasing number of dimensions. Previous approaches for …

We present a system to analyze time‐series data in sensor networks. Our approach supports
exploratory tasks for the comparison of univariate, geo‐referenced sensor data, in particular …

Color is one of the most important visual variables since it can be combined with any other
visual map** to encode information without using additional space on the display …

Pre‐processing is a prerequisite to conduct effective and efficient downstream data analysis.
Pre‐processing pipelines often require multiple routines to address data quality challenges …