Clustering Web Pages Sequences With Artificial Ants
Résumé
This paper introduces new web usage mining tools designed to help characterizing user accesses on websites. Our approach relies on a categorization of web user sessions to help identifying and understanding major trends in the navigations. The novelty of our work mainly relies in the clustering method that is a fast unsupervised ant-inspired clustering algorithm paired with new similarity measures that handle sessions either as a sequence or an unordered set of web pages. Our algorithm is evaluated on real web log files of a French museum that contains more than 39000 user sessions over one month and a half. The evaluation is conducted according to the quality of the output partitions and the interpretability of each cluster based on its content. Our experiments show that our algorithm can build meaningful sessions clusters that can help infer Web users’ motivations.