Parallel Coordinates ++
CS 4460/7450 - Information Visualization
Feb. 2, 2010
John Stasko
Last Time
• Viewed a number of techniques for
portraying low-dimensional data (about
3<x<20)
 scatterplot matrix
 Table Lens
 sliding rods
 Attribute Explorer
 Dust & Magnet
 etc.
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Parallel Coordinates
• What are they?
 Explain…
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Parallel Coordinates
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Parallel Coordinates
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Parallel Coordinates
Encode variables along
a horizontal row
Vertical line specifies different
values that variable can take
Data point represented as a
polyline
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Parallel Coords Example
Basic
Grayscale
Color
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Issue
• Different variables can have values taking
on quite different ranges
• Must normalize all down (e.g., 0->1)
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Application
• System that uses parallel coordinates for
information analysis and discovery
• Interactive tool
 Can focus on certain data items
 Color
Taken from:
A. Inselberg, “Multidimensional Detective”
InfoVis „97, 1997.
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Discuss
• What was their domain?
• What was their problem?
• What were their data sets?
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The Problem
• VLSI chip manufacture
• Want high quality chips (high speed) and
a high yield batch (% of useful chips)
• Able to track defects
• Hypothesis: No defects gives desired chip
types
• 473 batches of data
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The Data
• 16 variables
 X1 - yield
 X2 - quality
 X3-X12 - # defects (inverted)
 X13-X16 - physical parameters
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Parallel Coordinate Display
yield &
quality
defects
parameters
Yikes!
But not
that bad
Distributions
x1 - normal
x2 - bipolar
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Top Yield & Quality
split
defects
Have some defects
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Minimal Defects
Not the
highest
yields and
quality
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Best Yields
Appears that
some defects
are necessary
to produce
the best chips
Non-intuitive!
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XmdvTool
Toolsuite created
by Matthew Ward
of WPI
Includes parallel
coordinate views
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ParVis System
Demo
http://www.mediavirus.org/parvis/
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Challenges
Too much
data
Out5d dataset (5 dimensions, 16384 data items)
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(courtesy of J. Yang)
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Dimensional Reordering
Which dimensions are most like each other?
Same dimensions ordered according to similarity
Yang et al
InfoVis ‟03
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Dimensional Reordering
Can you reduce
clutter and highlight
other interesting
features in data by
changing order of
dimensions?
Peng et al
InfoVis „04
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Reducing Density
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Jerding and Stasko, ‟95, ‟98
Wegman & Luo, ‟96
Artero et al, 04
Johansson et al, „05
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Improved Interaction
• How do we let the user select items of
interest?
• Obvious notion of clicking on one of the
polylines, but how about something more
than that
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Attribute Ratios
• Angular Brushing
 Select subsets which exhibit a correlation
along 2 axes by specifying angle of interest
Hauser, Ledermann, & Doleisch
InfoVis „02
(earlier demo)
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Range Focus
• Smooth Brushing
 Specify a region of interest along one axis
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Combining
• Composite Brushing
 Combine brushes and DOI functions using
logical operators
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Video
http://www.vrvis.at/via/research/ang-brush/parvis4.mov
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Application
http://www.syracuse.com/news/index.ssf/2010/01/data_mining_helps_new_york_cat.html
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Different Kinds of Data
• How about categorical data?
 Can parallel coordinates handle that well?
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Parallel Sets
• Visualization method adopting parallel
coordinates layout but uses frequencybased representation
• Visual metaphor
 Layout similar to parallel coordinates
 Continuous axes replaced with boxes
• Interaction
 User-driven: User can create new
Kosara, Bendix, & Hauser
classifications
TVCG „05
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Representation
Color used for different
categories
Those values flow into the
other variables
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Example
Titanic passengers
data set
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Titanic Data Set
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Interactions
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Video
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InfoVis „05
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Star Plots
Var 1
Var 5
Var 2
Value
Var 4
Each “spoke” encodes
a variable‟s value
Var 3
Alternative Rep.
Spring 2010
Space out the n
variables at equal
angles around a
circle
Data point is now a “shape”
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Star Plot examples
http://seamonkey.ed.asu.edu/~behrens/asu/reports/compre/comp1.html
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Star Coordinates
• Same ideas as star plot
• Rather than represent point as polyline,
just accumulate values along a vector
parallel to particular axis
• Data case then becomes a point
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Star Coordinates
E. Kandogan, “Star Coordinates: A Multi-dimensional Visualization
Technique with Uniform Treatment of Dimensions”, InfoVis 2000
Late-Breaking Hot Topics, Oct. 2000
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Demo
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Star Coordinates
• Data cases with similar values will lead to
clusters of points
• (What‟s the problem though?)
• Multi-dimensional scaling or projection
down to 2D
• Good segue to next time…
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Parallel Coordinates
• Technique
 Strengths?
 Weaknesses?
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Design Project
• For graduate students
• Group of 2-4 students
 Can argue for singleton
• First milestone: Teams and topics in 2
weeks
• Choose from list of topics
 Can argue for your own
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Upcoming
• High-dimensional data representations
 Reading:
Yang et al, InfoVis ‟04
• InfoVis Systems & Toolkits
 Reading
Viegas et al, TVCG „07
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