1. Visualizing Document Collections
cs5764: Information Visualization
Chris North

2. Where are we? Multi-D 1D 2D 3D Trees Graphs Document collections
Design Principles
Empirical Evaluation
Visual Overviews

3. Structured Document Collections
Multi-dimensional
author, title, date, journal, …
Trees
Dewey decimal
Graphs
web, citations

4. Envision
Ed Fox, et al.
Multi-D
similar to Spotfire

5. Citation Networks Butterfly Browser Mackinlay et al (PARC) Butterfly:
Left = refs
Right = citers
Yellow = #citers
Blue = visited
3d plot:
date,
Name,
# citers

7. Unstructured Document Collections
Focus on Full Text
Examples:
digital libraries, news archives, web pages
archives, image galery
Tasks:
search
Browse
Classification, structurization
Statistics, keyword usage, languages
Subjects, themes, coverage

8. Visualization Strategies
Cluster Maps
Keyword Query
Relationships
Reduced representation
User controlled layout

9. Cluster Map Create a “map” of the document collection
Similar documents near each other
Dissimilar document far apart
“Grocery store” concept

10. Document Vectors Similarity between pair of docs =
Doc1 Doc2 Doc …
“aardvark”
“banana”
“chris” …
Similarity between pair of docs =
dot product
Layout documents in 2-D map by similarity
similar to spring model for graph layout

11. Cluster Algorithms Partition clustering: Partition into k subsets
Pick k seeds
Iteratively attract nearest neighbors
Hierarchical clustering: Dendrogram
Group nearest-neighbor pair
Iterate

12. Landscapes Wise et al, “Visualizing the non-visual”
ThemeScapes, Cartia
PNNL
Mountain height = Cluster size

13. Kohonen Maps Xia Lin, “Document Space”

15. WebSOM

16. Map.net

17. Galaxy of
News
MIT
Cluster map
with full text
zooming

18. Cluster Map 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

19. Keyword Query Keyword query, Search engine “Information Retrieval”
Rank ordered list
“Information Retrieval”
Visualization of results

20. Keyword Distributions
Hearst, “TileBars”
Keyword distributions within documents

21. Document Distributions
Korfhage, “VIBE”
Documents located between query keywords using spring model

22. VR-VIBE

23. Keyword Query 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

24. Relationships Show inter-relationships Matrix or Complete Graph Salton
Similarity measure between all pairs of docs
Threshold level
Salton

25. Variations
Docs + Paragraphs Themes

26. Relationships 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

27. Reduced Visual Representation
Bederson, “Image browsing”

28. User Controlled Layout
Card, “WebBook and Web Forager”

29. Data Mountain
Robertson, “Data Mountain” (Microsoft)