1. cs5984: Information Visualization Chris North
Document Collections 2
cs5984: Information Visualization
Chris North

2. Approaches
Clustering (last time)
Themescapes, …
Network
Keyword

3. Clustering
With
Full text
Galaxy of
News
pg 452

4. Clustering Good: Bad: 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

5. Network Show inter-relationships Matrix or Complete Graph
Similarity measure between all pairs of docs
Threshold level
Salton, pg 413

6. Variations
Docs + Paragraphs Themes

7. 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

8. Combination: Thinkmap

9. Keyword Search engine, keyword query “Information Retrieval”
Rank ordered list
“Information Retrieval”

10. Today
Hearst, “Tilebars”, web
umer, ashwini

11. VIBE Korfhage, http://www.pitt.edu/~korfhage/interfaces.html
Documents located between query keywords using spring model

12. VR-VIBE

13. InfoCrystal Spoerri, pg 140 Venn Diagram, all possible combinations
A&B&C&D
A&C&D
C&B C

14. Keyword Good: Bad: 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

15. Assignment Mid-Project status report: due today Read for Thurs
Fox, “Envision”, web, video
aejaaz, ravi

16. 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, ?