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 six 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

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 News

A catalogue of lay people’s information needs about AI systems

Wie Künstliche Intelligenz neue Wirkstoffe findet

Künstliche Intelligenz soll bei der Wirkstoffentwicklung die metaphorische Nadel im Heuhaufen finden. Ein Interview mit Prof. Nils M. Kriege in der Kurier...

Four papers accepted at ICDM 2024

Four papers by various members of the Data Mining and Machine Learning group have been accepted at the IEEE International Conference on Data Mining (ICDM 2024)

Jointly organized International Workshop on Mining and Learning with Graphs AND two amazing paper presentations

 

Our papers "On the Two Sides of Redundancy in Graph Neural Networks" and "Approximating the Graph Edit Distance with Compact Neighborhood Representations" have been accepted...

Paper accepted at KDD 2024

Our paper "Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent" has been accepted at the SIGKDD Conference on Knowledge Discovery and Data...