An organic user interface for searching citation links
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Visualizing a discipline: an author co-citation analysis of information science, 1972–1995
Journal of the American Society for Information Science
visualising semantic spaces and author co-citation networks in digital libraries
Information Processing and Management: an International Journal - Special issue on progress toward digital libraries
ACM Computing Surveys (CSUR)
Visualization of bibliographic networks with a reshaped landscape metaphor
VISSYM '02 Proceedings of the symposium on Data Visualisation 2002
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CASCON '96 Proceedings of the 1996 conference of the Centre for Advanced Studies on Collaborative research
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SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
EncCon: an approach to constructing interactive visualization of large hierarchical data
Information Visualization
Journal of the American Society for Information Science and Technology
Scientific authoring support: a tool to navigate in typed citation graphs
CL&W '10 Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing: Writing Processes and Authoring Aids
Citation chain aggregation: an interaction model to support citation cycling
Proceedings of the 20th ACM international conference on Information and knowledge management
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This paper describes an ongoing technique to collecting, mining, clustering and visualizing scientific articles and their relations in information science. We aim to provide a valuable tool for researchers in quick analyzing the relationship and retrieving the relevant documents. Our system, called CAVis, first automatically searches and retrieves articles from the Internet using given keywords. These articles are next converted into readable text documents. The system next analyzes these documents and it creates similarity matrix. A clustering algorithm is then applied to group the relevant papers into corresponding clusters. Finally, we provide a visual interface so that users can easily view the structure and the citing relations among articles. From the view, they can navigate through the collection as well as retrieve a particular article.