  The axes ordering in PCP presents a particular story from the data based on
the user perception of PCP polylines. Existing works focus on directly
optimizing for PCP axes ordering based on some common analysis tasks like
clustering, neighborhood, and correlation. However, direct optimization for PCP
axes based on these common properties is restrictive because it does not
account for multiple properties occurring between the axes, and for local
properties that occur in small regions in the data. Also, many of these
techniques do not support the human-in-the-loop (HIL) paradigm, which is
crucial (i) for explainability and (ii) in cases where no single reordering
scheme fits the user goals. To alleviate these problems, we present PC-Expo, a
real-time visual analytics framework for all-in-one PCP line pattern detection,
and axes reordering. We studied the connection of line patterns in PCPs with
different data analysis tasks and datasets. PC-Expo expands prior work on PCP
axes reordering by developing real-time, local detection schemes for the 12
most common analysis tasks (properties). Users can choose the story they want
to present with PCPs by optimizing directly over their choice of properties.
These properties can be ranked, or combined using individual weights, creating
a custom optimization scheme for axes reordering. Users can control the
granularity at which they want to work with their detection scheme in the data,
allowing exploration of local regions. PC-Expo also supports HIL axes
reordering via local-property visualization, which shows the regions of
granular activity for every axis pair. Local-property visualization is helpful
for PCP axes reordering based on multiple properties, when no single reordering
scheme fits the user goals.
