Visualizing Document Collections

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Visualizing Document Collections

• cs5764 Information Visualization
• Chris North

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Where are we?

• Multi-D
• 1D
• 2D
• 3D
• Trees
• Graphs
• Document collections

• Design Principles
• Empirical Evaluation
• Visual Overviews

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Structured Document Collections

• Multi-dimensional
• author, title, date, journal,
• Trees
• Dewey decimal
• Graphs
• web, citations

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Envision

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

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Citation Networks

• Butterfly Browser
• Mackinlay et al (PARC)

Butterfly Left refs Right citers Yellow
citers Blue visited 3d plot date, Name,
citers
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Unstructured Document Collections

• Focus on Full Text
• Examples
• digital libraries, news archives, web pages
• email archives, image galery
• Tasks
• search
• Browse
• Classification, structurization
• Statistics, keyword usage, languages
• Subjects, themes, coverage

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Visualization Strategies

• Cluster Maps
• Keyword Query
• Relationships
• Reduced representation
• User controlled layout

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Cluster Map

• Create a map of the document collection
• Similar documents near each other
• Dissimilar document far apart
• Grocery store concept

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Document Vectors

• Doc1 Doc2 Doc3
• aardvark 1 2 0
• banana 2 1 0
• chris 0 0 3
• Similarity between pair of docs
• dot product
• Layout documents in 2-D map by similarity
• similar to spring model for graph layout

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Cluster Algorithms

• Partition clustering Partition into k subsets
• Pick k seeds
• Iteratively attract nearest neighbors
• Hierarchical clustering Dendrogram
• Group nearest-neighbor pair
• Iterate

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Landscapes

• Wise et al, Visualizing the non-visual
• ThemeScapes, Cartia
• PNNL
• Mountain height Cluster size

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Kohonen Maps

• Xia Lin, Document Space
• http//faculty.cis.drexel.edu/sitemap/index.html

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WebSOM

• http//websom.hut.fi/websom/

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Map.net

• http//maps.map.net/start

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• Galaxy of
• News
• MIT
• Cluster map
• with full text
• zooming

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Cluster Map

• 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

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Keyword Query

• Keyword query, Search engine
• Rank ordered list
• Information Retrieval
• Visualization of results

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Keyword Distributions

• Hearst, TileBars
• http//elib.cs.berkeley.edu/tilebars/
• Keyword distributions within documents

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Document Distributions

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

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VR-VIBE
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Keyword Query

• Good
• Reduces the browsing space
• Map according to users interests

• Bad
• What keywords do I use?
• What about other related documents that dont use
  these keywords?
• No initial overview
• Mega-hit, zero-hit problem

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Relationships

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

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Variations

• Docs Paragraphs Themes

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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

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Reduced Visual Representation

• Bederson, Image browsing

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User Controlled Layout

• Card, WebBook and Web Forager
• http//vtopus.cs.vt.edu/north/infoviz/webbook.mpa
  

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Data Mountain

• Robertson, Data Mountain (Microsoft)