Visual Web Mining Amir H. Youssefi David J. Duke Mohammed J. Zaki Analysis of web site usage data involves two significant challenges: firstly the volume of data, arising from the growth of the web, and secondly, the structural complexity of web sites. In this paper we apply Data Mining and Information Visualization techniques to the web domain in order to benefit from the power of both human visual perception and computing; we term this Visual Web Mining. In response to the two challenges, we propose a generic framework, where we apply Data Mining techniques to large web data sets and use Information Visualization methods on the results. The goal is to correlate the outcomes of mining Web Usage Logs and the extracted Web Structure, by visually superimposing the results. We propose several new information visualization diagrams and analyze their utility and elaborate on the architecture of a prototype implementation. Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY cs-03-16
Visual Web Mining
Amir H. Youssefi
David J. Duke
Mohammed J. Zaki
Analysis of web site usage data involves two significant challenges: firstly the volume of data, arising from the growth of the web, and secondly, the structural complexity of web sites. In this paper we apply Data Mining and Information Visualization techniques to the web domain in order to benefit from the power of both human visual perception and computing; we term this Visual Web Mining. In response to the two challenges, we propose a generic framework, where we apply Data Mining techniques to large web data sets and use Information Visualization methods on the results. The goal is to correlate the outcomes of mining Web Usage Logs and the extracted Web Structure, by visually superimposing the results. We propose several new information visualization diagrams and analyze their utility and elaborate on the architecture of a prototype implementation.
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY
cs-03-16