  We present an integrated approach for creating and assigning color palettes
to different visualizations such as multi-class scatterplots, line, and bar
charts. While other methods separate the creation of colors from their
assignment, our approach takes data characteristics into account to produce
color palettes, which are then assigned in a way that fosters better visual
discrimination of classes. To do so, we use a customized optimization based on
simulated annealing to maximize the combination of three carefully designed
color scoring functions: point distinctness, name difference, and color
discrimination. We compare our approach to state-ofthe-art palettes with a
controlled user study for scatterplots and line charts, furthermore we
performed a case study. Our results show that Palettailor, as a fully-automated
approach, generates color palettes with a higher discrimination quality than
existing approaches. The efficiency of our optimization allows us also to
incorporate user modifications into the color selection process.
