Transcript Overview and Detail

Multivariate Visualization
Tools
CS 7450 - Information Visualization
January 25, 2005
John Stasko
Last Time
• We examined a number of
techniques/visualizations for presenting
multivariate (typically >3) data sets
 For < 3 variables, the tried and true charting
techniques are hard to beat
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Techniques
Scatterplot Matrix
Chernoff Faces
Star Coordinates
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Star Plots
Parallel Coordinates
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Today
• One more interesting technique
• Systems designed for hypervariate data
sets
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Another Technique
• Database of data items, each of n
dimensions
• Issue a query that specifies a target value
of the dimensions
• Often get back no exact matches
• Want to find near matches
Taken from:
D. Keim, H-P Kriegel, “VisDB Database Exploration
Using Multid Vis”, IEEE CG&A, 1994.
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Relevance Factor
• How close an item is to the query
• Data items have some value that can be
numerically quantified
• Each dimension is some distance away
from query item
• Sum these up for total distance
• Relevance is inverse of distance
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Example
• 5 dimensions, integers 0->255
• Query:
6, 210, 73, 45, 92
• Data item: 8, 200, 73, 50, 91
• Distance:
2 + 10 + 0 + 5 + 1 = 18
• Relevance: 1275 - 18 = 1267
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Issues
• What if dimensions are real numbers or
text strings?
• What if they’re the same type, but of
different orders of magnitude?
• Have to define some kind of distance,
then a weight function to multiply by
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Technique
• Calculate relevance of all data points
• Sort items based on relevance
• Use spiral technique to order the values
• Color items based on relevance
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Relevance Colors
Low
High
Empirically established
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Spiral Method
Highest relevance
value in center,
decreasing values
grow outward
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Display Methodology
Example: five-dimensional data
Total
relevance
Dim 1
Dim 2
Spiral
in each
window
Dim 5
Dim 4
Same item
appears in
same place
in each
window
Dim 3
Items ordered by total relevance
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Figure from Paper
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Example Display
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Alternative
• Grouping arrangement
• Doesn’t use multiple windows
• Create all relevance dimensional
depictions for an item and group them
• Spiral out the different data items’
depictions
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Grouping Arrangement
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Example Display
8 dimensions
1000 items
Multi-window
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Grouping
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Tools
• Examine systems that have been built to
visualize (relatively) high-dimensional data
sets
 Use some of these existing techniques and
add new ones
 Interactivity is a key
• Demos of each of the systems
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HW 4
• Work with, understand, and critique these
systems
 Data sets
 Consider user tasks
 Submit a quality analysis
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Spotfire
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SeeIt
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InfoZoom
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Eureka
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Documentation
• Some handed out
• CoWeb has a tips page
• Manuals where papers are
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Other Systems
• A number of other systems exist
 Some that will not be in this particular HW…
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EZChooser
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Grokker
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ILOG Discovery
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HW 1
• Interesting ones
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HW 2
• Trends I noticed and interesting ones…
 Notion of a particular visualization for this
problem (design contribution) versus a more
general visualization that could apply to other
areas (research contribution)
• We’re gradually learning more that should
be helpful with such future tasks
• Grading
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HW 3
• So what’s planned?
• What did you think?
 Difficult?
 Challenges?
• We’ll talk more about it after I’ve looked
at them
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Upcoming
• Visual perception
 Reading:
Chapter 4
• Tufte’s principles
 Be reading his book
 He’s coming to Atlanta
https://www.edwardtufte.com/tufte/courses
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