Visualization of large category map for internet browsing

  • Authors:

  • Christopher C. Yang;Hsinchun Chen;Kay Hong

  • Affiliations:

  • Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China;Department of Management Information Systems, The University of Arizona, Tucson, AZ;Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China

  • Venue:
  • Decision Support Systems - Web retrieval and mining
  • Year:
  • 2003

Quantified Score

Hi-index 0.00

Visualization

Abstract

Information overload is a critical problem in World Wide Web. Category map developed based on Kohonen's self-organizing 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.