Our research is inspired by the challenges of changing environments. We develop generic methods for model adaption and for change monitoring, and dedicated methods in medical mining and in business mining.
In the thematic area Medical Mining we develop mining methods for the analysis of cohort data from epidemiological and clinical studies. Together with the University Medicine Greifswald we work on learning classification models, on the identification of subpopulations with increased disease prevalence, on the characterization of such subpopulations, and on reducing the high-dimensional feature space in a semi- supervised way. Together with the Diabetology of the OVGU University Medicine, we work on analysing stance patterns for patients with diabetic foot syndrome. Together with the University Hospital Regensburg and the University of Ulm we study the evolution of patients suffering from tinnitus. More on our cooperations and initiatives on Medical Mining can be found here.
In the thematic Area Business Mining we develop stream mining methods for polarity classification and topic monitoring on opinionated document streams and for dynamic recommendation engines. Opinion stream mining is the subject of the project . Learning on a stream of ratings and adaptation of the recommender core is investigated under Dynamic Recommenders.
In the project OSCAR we build stream mining methods to capture the evolution of a stream of opinionated documents. Building upon the results of the earlier project IMPRINT, we strive to develop supervised and semi-supervised (passive and active) learning algorithms that can exploit inputs from the human expert, as well as historical data on the words and entities observed in the stream. More on OSCAR is here.
A detailed list of current and completed projects can be found on the reseach portal of Saxony-Anhalt, click here (in German).