Présentation personnalisée des informations environnementales
We present our work in this thesis in the field of information visualization. We dealt with the problem of the generation of weather forecasts reports. Given the huge amount of information produced by Environment Canada and a wide variety of users, it must generate a customized visualization that meets their needs and preferences.  We developed MeteoVis, a weather report generator. Given that we have little information on the user profile, we relied on the choices made by similar users to calculate the needs and preferences of a user.  We use unsupervised machine learning techniques to group similar users . We compute a degree of similarity of user profiles in the same cluster to determine the needs and preferences.  We conducted, with the help of external users experiments for evaluating and comparing our tool with the current site of Environment Canada. The evaluation results show that the visualizations generated by MeteoVis are significantly better than the current bulletins prepared by EC.
Information visualization Personalization User profile Machine learning Data mining Graph