PARALLEL
SETS: Interactive Exploration and
Visual Analysis of Categorical Data
Robert
Kosara, Fabian Bendix and Helwig
Hauser
Abstract—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
UNCC Technical Report #CVC-UNCC-06-10