  Line-based density plots are used to reduce visual clutter in line charts
with a multitude of individual lines. However, these traditional density plots
are often perceived ambiguously, which obstructs the user's identification of
underlying trends in complex datasets. Thus, we propose a novel image space
coloring method for line-based density plots that enhances their
interpretability. Our method employs color not only to visually communicate
data density but also to highlight similar regions in the plot, allowing users
to identify and distinguish trends easily. We achieve this by performing
hierarchical clustering based on the lines passing through each region and
mapping the identified clusters to the hue circle using circular MDS.
Additionally, we propose a heuristic approach to assign each line to the most
probable cluster, enabling users to analyze density and individual lines. We
motivate our method by conducting a small-scale user study, demonstrating the
effectiveness of our method using synthetic and real-world datasets, and
providing an interactive online tool for generating colored line-based density
plots.
