index - ADVanced Analytics for data SciencE Access content directly

The research activities carried out by the ADVANSE team are in the field of the analysis of large databases in order to extract new knowledge. They relate to the following three areas:

  • Data mining (DM) which continues historical work with particular emphasis on the proposal of pattern mining approaches integrating either new dimensions such as, for example, the spatial dimension in the case of spatio-temporal patterns and trajectories or else the dimensions associated with the different types of arcs which may exist in multigraphs;
  • Analytical Visualization (VA) which emphasizes analytical reasoning facilitated by interactive visual interfaces. Such interfaces, integrating various methods of information representation, interaction or even automatic knowledge extraction, aim to allow users to extract, from complex and / or heterogeneous data, information directly supporting analysis, planning and decision making;
  • Machine learning (ML) which, in addition to the various works carried out on the definition of new machine learning approaches (eg detection of rare clusters, labeling of topics), focuses on taking into account small or very large data sets via traditional approaches (eg SVM, Gradient Boosting, active learning) or more recent (eg deep learning). Indeed, Deep Learning has shown its effectiveness for classifying data in large volumes or with large numbers of dimensions (e.g. images, sounds, texts). However, the explicability of the results, or even the definition of a good architecture remain very experimental exercises and constitute real challenges for the scientific community.

The ADVANSE team, in its three axes, is developing work on both theoretical and experimental bases to address the associated issues. The team is taken to cross these different axes for various projects.

Open Access Files

84 %

Number of full texts


Number of records


Publishers' policy on open archives

Mapping of collaborations


Corpus Intelligence artificielle ANALYSE DE DONNEES Species distribution model NLP TAL Aménagement du territoire Emotion analysis Training Cancer du sein Algorithms Remote sensing Médias sociaux Visualisation Classification Learning Réseaux sociaux Machine learning Neural networks Analyse d’opinions Automatic Term Extraction Species prediction Données textuelles Model performance Benchmark Semantic annotation Managers Réalité Virtuelle Sequential patterns Species identification Environmental data Données multi-sources Multilayer graphs Ontology Data mining Evaluation Graphs Fouille de données Machine Learning Apprentissage supervisé Information visualization Nlp Satellite image time series Natural language processing Analyse de sentiments Compétences sociales ALGORITHME Biodiversity Text mining Biomedical ontologies Natural Language Processing Hydro-ecology Prediction Visual analytics Recommendation Social networks BioNLP Clustering Commerciaux Species distribution models Fouille de textes Artificial intelligence Fouille de texte Information extraction Virtual Reality Social media Base de données intégrée Controversy detection Methods comparison Graph neural networks Web Mining LifeCLEF Diversity Polarity detection Top-k Citizen science Text categorization Text Mining Sentiment analysis Angular Categorical data Aggregation Visualization Méta-descripteurs Visualisation d'information Médecine Opinion mining Animal epidemiology Signal processing Data Mining Predictive power Apprentissage Graph Mining Bird identification Deep learning SMS Presence-only data Species distribution Plant identification Suicide