Head of team
Alexandre TERMIER (Enseignant-chercheur Université de Rennes 1)

LACODAM, Large Scale Collaborative Data Mining

The research objective is to greatly facilitate the process of making meaning from large amounts of data, either by deriving new knowledge or by making better decisions. Currently, this process is largely manual and relies on the analyst's understanding of the field, the available data, and a plethora of complex computational tools.

The LACODAM team is aiming at a new generation of approaches to data analysis where the different ways of discovering the underlying structure of the data are explored automatically, and only the most relevant structures are presented to the analyst. Such a notion of relevance depends strongly on the knowledge of the domain and the analyst's own knowledge: this type of knowledge will be central to our approach.

The solutions envisaged by the team require bridging the gap between data mining techniques and artificial intelligence approaches, both to take into account domain knowledge in a generic way and to introduce formal reasoning techniques in knowledge discovery workflows.

In addition, in order to acquire as much knowledge as possible, LACODAM is interested in community-based approaches, addressing communities of analysts and practitioners working on a particular domain, sharing data sets, knowledge and results, and making feedback available to the community.


Campus de Beaulieu, RENNES (35)
Activity reports
Attachment Size
lacodam2019.pdf 508.44 KB
lacodam2018.pdf 502.12 KB
lacodam2017.pdf 495.88 KB