Knowledge Discovery & Data Mining

Visual analytics, classifiers, ontologies, modelling and simulation

Knowledge Discovery & Data Mining (KDD) techniques focus on the computer-supported extraction of useful knowledge from data and information. They help to discover and identify patterns in data that are hidden, not evident –and sometimes unexpected-, making them understandable to people and providing useful insights (which is especially relevant in large repositories where the human resources available may be limited). The use of KDD in multimedia-based applications is a powerful mechanism for improving understanding and adding value to large repositories of multimedia information.

  • Visual Analytics allows the analytical reasoning of complex data facilitated by interactive visual interfaces.
  • Classifiers and other Pattern Recognition technologies allow classifying information according to predefined criteria. The information may vary in nature: visual, textual, acoustic, etc.
  • Ontologies are explicit computer representations of knowledge in a specific domain (the explicit specification of the conceptualization of that domain). Ontologies are a key technology in Semantic Web and other Artificial Intelligence applications.
  • Modelling and Simulation, in which complex models of engineering and science processes can be analyzed and interactively visualized, along with the modeling and simulation of users’ behavior and preferences (user profiling).





Outstanding projects