Web page clustering using a self-organizing map of user navigation patterns

Authors:
Kate A. Smith;Alan Ng
Affiliations:
School of Business Systems, Monash University, P.O. Box 63B, Victoria 3800, Australia;School of Business Systems, Monash University, P.O. Box 63B, Victoria 3800, Australia
Venue:
Decision Support Systems - Special issue: Web data mining
Year:
2003

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Abstract

The continuous growth in the size and use of the Internet is creating difficulties in the search for information. A sophisticated method to organize the layout of the information and assist user navigation is therefore particularly important. In this paper, we evaluate the feasibility of using a self-organizing map (SOM) to mine web log data and provide a visual tool to assist user navigation. We have developed LOGSOM, a system that utilizes Kohonen's self-organizing map to organize web pages into a two-dimensional map. The organization of the web pages is based solely on the users' navigation behavior, rather than the content of the web pages. The resulting map not only provides a meaningful navigation tool (for web users) that is easily incorporated with web browsers, but also serves as a visual analysis tool for webmasters to better understand the characteristics and navigation behaviors of web users visiting their pages.