Clustering incrémental et méthodes de détection de nouveauté : application à l'analyse intelligente d'informations évoluant au cours du temps

Learning algorithms proved their ability to deal with large amount of data. Most of the statistical approaches use defined size learning sets and produce static models. However in specific situations: active or incremental learning, the learning task starts with only very few data. In that case, looking for algorithms able to produce models with only few examples becomes necessary. The literature's classifiers are generally evaluated with criteria such as: accuracy, ability to order data (ranking)... But this classifiers' taxonomy can really change if the focus is on the ability to learn with just few examples. To our knowledge, just few studies were performed on this problem. This study aims to study a larger panel of both algorithms (9 different kinds) and data sets (17 UCI bases)