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Document Collections cs5984: Information Visualization Chris North Where are we? • • • • • • • Multi-D 1D 2D Hierarchies/Trees Networks/Graphs Document collections 3D • • • • • • Design Principles Empirical Evaluation Java Development Visual Overviews Multiple Views Peripheral Views Structured Document Collections • Multi-dimensional • author, title, date, journal, … • Trees • dewey decimal • Networks • web, citations Envision • Ed Fox, et al. • Multi-D • similar to Spotfire Unstructured Document Collections • Focus on Full Text • Examples: • digital libraries, encyclopedia • Web, homepages, photo collections • Tasks: • • • • search, keyword Browse Themes, subjects, topics, library coverage Size, distributions Visualization Strategies • • • • • today Cluster Maps Keyword Query today Relationships Reduced representation User controlled layout Cluster Map • Create a “map” of the document collection • Similar documents near • Dissimilar document far • “Grocery store” concept Document Vectors • • • • “aardvark” “banana” “chris” … Doc1 1 2 0 Doc2 2 1 0 Doc3 0 0 3 • Similarity between pair of docs = • • Layout documents in 2-D map by similarity • similar to spring model for graph layout … Cluster Algorithms • Partition clustering: Partition into k subsets • Pick k seeds • Iteratively attract nearest neighbors • Hierarchical clustering: Dendrogram • Group nearest-neighbor pair • Iterate Kohonen Maps • Xia Lin, “Document Space” • samal, ying • http://faculty.cis.drexel.edu/sitemap/index.html Themescapes, Cartia • PNL • Mountain height = Cluster size WebSOM • http://websom.hut.fi/websom/ Map.net • http://maps.map.net/start 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 Keyword Query • Keyword query, Search engine • Rank ordered list • “Information Retrieval” Tilebars • Hearst, “Tilebars” • reenal, xueqi • http://elib.cs.berkeley.edu/tilebars/ VIBE • Korfhage, http://www.pitt.edu/~korfhage/interfaces.html • Documents located between query keywords using spring model VR-VIBE Keyword Query • 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 • Thurs: Document Collections • Bederson, “Image Browsing” » Rui, anusha • Card, “Web Book and Web Forager” » mrinmayee, ming • Demo your hw3: tues or thurs Next Week • Tues: 3-D data • Kniss, “Interactive Volume Rendering with Direct Manip” » xueqi, mahesh • Thurs: Workspaces • Robertson, “Task Gallery” » supriya, varun • Upson, “AVS” » christa, jun • Thanksgiving break • Tues 27: Debates • Kobsa, “Empirical comparison of comm infovis systems” » kunal, zhiping Upcoming Sched • • • • • • Tues: 3-D data Thurs: Workspaces Thanksgiving break Tues 27: Debates Thurs 29: How (not) to lie with visualization Dec: project presentations • Dec 7: CHI 2-pagers due, student posters due