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

 News

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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!

Paper accepted at ICDM 2021

Our paper "Density-Based Clustering for Adaptive Density Variation" has been accepted at ICDM 2021.

SIGKDD 2021 Best Student Paper Award

Our paper "Spectral Clustering of Attributed Multi-relational Graphs" received the SIGKDD 2021 Best Student Paper Award

Review of the Kaiserschild Lectures with Prof. Claudia Plant "AI in Medicine" [german]

On 15 April, experts discussed the question of whether artificial intelligence can revolutionise medicine as part of the Kaiserschild Lectures series. Among the panelists:...

Two papers accepted at KDD 2021

We are happy to announce that two papers have been accepted at KDD (acceptance rate 15%)

Paper accepted at IJCAI 2021

Our paper "Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces" has been accepted for presentation at IJCAI-21 (13.9% acceptance rate).

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