Clustering frequent navigation patterns from website logs by using ontology and temporal information

In this work, clustering algorithms are used in order to group similar frequent sequences of Web page visits. A new sequence is compared with all clusters and it is assigned to the most similar one. This work can be used for predicting and prefetching the next page user will visit or for helping the navigation of user in the website. They can also be used to improve the structure of website for easier navigation. In this study the effect of time spent on each web page during the session is also analyzed. © 2013 Springer-Verlag London