Categorical data dimensions appear in many
real-world data sets, but few visualization methods exist
that properly deal with them.

Parallel Sets are a new method for the visualization
and interactive exploration of categorical data that shows
data frequencies instead of the individual data points. The
method is based on the axis layout of parallel coordinates,
with boxes representing the categories and parallelograms
between the axes showing the relations between categories.
In addition to the visual representation, we designed
a rich set of interactions. Parallel Sets allow the user to
interactively remap the data to new categorizations, and
thus to consider more data dimensions during exploration
and analysis than usually possible. At the same time, a
meta-level, semantic representation of the data is built.
Common procedures, like building the cross product of
two or more dimensions, can be performed automatically,
thus complementing the interactive visualization.

We demonstrate Parallel Sets by analyzing a large CRM
data set, as well as investigating housing data of two US
states.