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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.

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