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


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Paper accepted at IEEE TAI

The paper "Replication Robust Payoff Allocation in Submodular Cooperative Games" has been accepted for publication in the IEEE Transactions on Artificial Intelligence.

Paper accepted at DSAA 2022

The paper "AWT - Clustering Meteorological Time Series Using an Aggregated Wavelet Tree" has been accepted at the DSAA 2022.


Paper accepted at NeurIPS 2022

The paper "Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited" has been accepted at the NeurIPS

Two papers accepted at ICDM 2022

Our papers "DBHD: Density-based clustering for highly varying density" and "Deep Clustering With Consensus Representations" have been accepted at IEEE ICDM 2022

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

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