Preface: Intelligent interactive data visualization

Abstract

The increasing amount and complexity of electronic data poses problems for userswho analyze that data. They cannot rely on fully automatic techniques for data analysis and visualization, because effective modeling requires an iterative interaction between computerized processing and human analysis. Such a human-in-the-loop approach enables users to interactively refine their hypotheses and modeling assumptions and arrive at conclusions that are impossible for the computer to reach on its own. Intelligent data visualization and its interaction with traditional machine learning serve a central role in this process. The aimof visual analytics is to develop intelligent interactive visualizations of data. Visual analytics intersectsmachine learning, intelligent systems, pattern analysis, visualization, computer graphics, and human computer interaction. As such, diverse fields are tackled ranging from the design and evaluation of intuitive data interfaces, efficient and effective data visualization algorithms, evaluation measures and experiment design for user studies, up to software systems for dedicated application areas such as bioinformatics. In this special issue, a variety of novel approaches of machine