Persistent Link:
http://hdl.handle.net/10150/106272
Title:
Visualization of large category map for Internet browsing
Author:
Yang, Christopher C.; Chen, Hsinchun; Hong, Kay
Citation:
Visualization of large category map for Internet browsing 2003-04, 35(1):89-102 Decision Support Systems
Publisher:
Elsevier
Journal:
Decision Support Systems
Issue Date:
Apr-2003
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106272
Submitted date:
2004-09-04
Abstract:
Information overload is a critical problem in World Wide Web. Category map developed based on Kohonenâ s selforganizing map (SOM) has been proven to be a promising browsing tool for the Web. The SOM algorithm automatically categorizes a large Internet information space into manageable sub-spaces. It compresses and transforms a complex information space into a two-dimensional graphical representation. Such graphical representation provides a user-friendly interface for users to explore the automatically generated mental model. However, as the amount of information increases, it is expected to increase the size of the category map accordingly in order to accommodate the important concepts in the information space. It results in increasing of visual load of the category map. Large pool of information is packed closely together on a limited size of displaying window, where local details are difficult to be clearly seen. In this paper, we propose the fisheye views and fractal views to support the visualization of category map. Fisheye views are developed based on the distortion approach while fractal views are developed based on the information reduction approach. The purpose of fisheye views are to enlarge the regions of interest and diminish the regions that are further away while maintaining the global structure. On the other hand, fractal views are an approximation mechanism to abstract complex objects and control the amount of information to be displayed. We have developed a prototype system and conducted a user evaluation to investigate the performance of fisheye views and fractal views. The results show that both fisheye views and fractal views significantly increase the effectiveness of visualizing category map. In addition, fractal views are significantly better than fisheye views but the combination of fractal views and fisheye views do not increase the performance compared to each individual technique.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Internet; Information Seeking Behaviors
Local subject classification:
National Science Digital Library; NSDL; Artificial Intelligence lab; AI lab; Internet browsing; Information visualization; Fisheye view; Fractal view; Category map; Information overloading; Visual load

Full metadata record

DC FieldValue Language
dc.contributor.authorYang, Christopher C.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorHong, Kayen_US
dc.date.accessioned2004-09-04T00:00:01Z-
dc.date.available2010-06-18T23:43:36Z-
dc.date.issued2003-04en_US
dc.date.submitted2004-09-04en_US
dc.identifier.citationVisualization of large category map for Internet browsing 2003-04, 35(1):89-102 Decision Support Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/106272-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractInformation overload is a critical problem in World Wide Web. Category map developed based on Kohonenâ s selforganizing map (SOM) has been proven to be a promising browsing tool for the Web. The SOM algorithm automatically categorizes a large Internet information space into manageable sub-spaces. It compresses and transforms a complex information space into a two-dimensional graphical representation. Such graphical representation provides a user-friendly interface for users to explore the automatically generated mental model. However, as the amount of information increases, it is expected to increase the size of the category map accordingly in order to accommodate the important concepts in the information space. It results in increasing of visual load of the category map. Large pool of information is packed closely together on a limited size of displaying window, where local details are difficult to be clearly seen. In this paper, we propose the fisheye views and fractal views to support the visualization of category map. Fisheye views are developed based on the distortion approach while fractal views are developed based on the information reduction approach. The purpose of fisheye views are to enlarge the regions of interest and diminish the regions that are further away while maintaining the global structure. On the other hand, fractal views are an approximation mechanism to abstract complex objects and control the amount of information to be displayed. We have developed a prototype system and conducted a user evaluation to investigate the performance of fisheye views and fractal views. The results show that both fisheye views and fractal views significantly increase the effectiveness of visualizing category map. In addition, fractal views are significantly better than fisheye views but the combination of fractal views and fisheye views do not increase the performance compared to each individual technique.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectInterneten_US
dc.subjectInformation Seeking Behaviorsen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial Intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherInternet browsingen_US
dc.subject.otherInformation visualizationen_US
dc.subject.otherFisheye viewen_US
dc.subject.otherFractal viewen_US
dc.subject.otherCategory mapen_US
dc.subject.otherInformation overloadingen_US
dc.subject.otherVisual loaden_US
dc.titleVisualization of large category map for Internet browsingen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalDecision Support Systemsen_US
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