Abstract
When a user visits a web site, important information concerning his/her preferences and behavior is stored implicitly in the associated log files. This information can be revealed by using data mining techniques and can be used in order to improve both, content and structure of the respective web site.
From the set of possible that define the visitor’s behavior, two have been selected: the visited pages and the time spent in each one of them. With this information, a new distance was defined and used in a self organizing map which identifies clusters of similar sessions, allowing the analysis of visitors behavior.
The proposed methodology has been applied to the log files from a certain web site. The respective results gave very important insights regarding visitors behavior and preferences and prompted the reconfiguration of the web site.
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Velásquez, J.D., Yasuda, H., Aoki, T., Weber, R., Vera, E. (2003). Using Self Organizing Feature Maps to Acquire Knowledge about Visitor Behavior in a Web Site. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_127
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DOI: https://doi.org/10.1007/978-3-540-45224-9_127
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