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


Franka presented the paper "Removing Redundancy in Graph Neural Networks" by Franka Bause, Samir Moustafa, Wilfried Gansterer, Nils M Kriege (Link:...

We are excited to announce that our paper "Interpretable Subgraph Feature Extraction for Hyperlink Prediction" has been accepted at the IEEE International Conference on Data...

Mit klugen Algorithmen die Qualität von ChatGPT messen []

Benjamin Roth untersucht, wie Algorithmen uns bei der Verarbeitung von großen Textmengen unterstützen können. Gemeinsam mit seinem Team entwickelt er Programme, mit denen man...

We are excited to announce that our paper "Specifying Prior Beliefs over DAGs in Deep Bayesian Causal Structure Learning" has been accepted at the European Conference on...

FFG BRIGDE Grant for the Data Mining and Machine Learning team

The project "CLU-Smart: Clustering of Smart-Meter Data" has been accepted by the FFG Bridge funding program.

ECML-PKDD 2023: chair and three accepted papers

Claudia Plant (PC) and Yllka Velaj (PhD Forum) serve as chairs of ECML-PKDD. Moreover, three research papers from our group have been accepted (acceptance rate: 24%)