The research group Data Mining and Machine Learning investigates novel approaches to exploratory data analysis, unsupervised, semi-supervised and supervised learning. We focus on methods for various data types including texts, graphs, high-dimensional feature vectors and other complex structures. We consider different tasks, e.g., representation learning, embedding, clustering, causality detection, classification and reinforcement learning.

The research group Data Mining and Machine Learning consists of four work groups:

Our methods are inspired by challenges arising from different application areas, e.g. medicine, neuroscience, pharmacoinformatics, renewable energies and social sciences.

Team of research group DM

Publications of research group DM

Projects of research group DM


Bei der diesjährigen Kinderuni hatten die kleinen Studierenden in der Eröffnungswoche gleich vier Mal die Möglichkeit etwas über Informatik und einige spezielle...

Paper accepted at ECML PKDD 2022

Our paper "EmbAssi: Embedding Assignment Costs for Similarity Search in Large Graph Databases" has been accepted at ECML PKDD 2022.

Paper accepted at ICML 2022

Our paper "Interactively Learning Preference Constraints in Linear Bandits" has been accepted at the International Conference on Machine Learning

Paper accepted at IJCAI 2022

Our paper "Option Transfer and SMDP Abstraction with Successor Features" has been accepted at the International Joint Conference on Artificial Intelligence (IJCAI)

Paper accepted at The Web Conference 2022

Our paper "Temporal Walk Centrality: Ranking Nodes in Evolving Networks" has been accepted at The Web Conference 2022

Franka Bause receives Hans Uhde Award

The Hans Uhde Prize is awarded to the best students of the TU Dortmund

University for outstanding theses in engineering. Congratulations, Franka!