Transcript Multi-Dimensional Data Visualization 2

Document Collections 2
cs5984: Information Visualization
Chris North
Approaches
• Clustering (last time)
• Themescapes, …
• Network
• Keyword
Clustering
With
Full text
Galaxy of
News
pg 452
Clustering
• Good:
•
•
•
•
Map of collection
Major themes and sizes
Relationships between themes
Scales up
• Bad:
• Where to locate documents with multiple themes?
» Both mountains, between mountains, …?
• Relationships between documents, within documents?
• Algorithm becomes (too) critical
Network
• Show inter-relationships
• Matrix or Complete Graph
• Similarity measure between all pairs of docs
• Threshold level
• Salton, pg 413
Variations
Docs + Paragraphs
Themes
Network
• Better for smaller, more detailed map
• Scale up: Network visualization
• Good:
• Can see more complex relationships between/within
documents
• Can act like hyperlinks!
• Bad:
• Finding specific documents
• Scale up difficult
Combination: Thinkmap
• http://www.thinkmap.com/article.cfm?articleID=38
Keyword
• Search engine, keyword query
• Rank ordered list
• “Information Retrieval”
Today
• Hearst, “Tilebars”, web
» umer, ashwini
VIBE
• Korfhage, http://www.pitt.edu/~korfhage/interfaces.html
• Documents located between query keywords using spring model
VR-VIBE
InfoCrystal
• Spoerri, pg 140
• Venn Diagram,
all possible
combinations
A&B&C&D
A&C&D
C&B
C
Keyword
• Good:
• Reduces the browsing space
• Map according to user’s interests
• Bad:
• What keywords do I use?
• What about other related documents that don’t use these
keywords?
• No initial overview
• Mega-hit, zero-hit problem
Assignment
• Mid-Project status report: due today
• Read for Thurs
• Fox, “Envision”, web, video
» aejaaz, ravi
Upcoming Weeks
• I’m at CHI all next week
• Tues: Go to VE, SciViz lab: Torg 3050
• Bowman, Kriz, Kelso
• Thurs: McCrickard
• Read for Tues Apr 10
• DeFanti, “Scientific Visualization”, pg 39
• Sayle, “Rasmol”, web
» Yuying, ?