Data Mining and AI

Profile

The Data Mining Research Group, led by Prof. Thomas Seidl, addresses current issues at the intersection of data mining, machine learning, and database systems. The research focuses on key topics such as clustering, active learning, multimodal and visual-language learning, process mining, and learning with limited labels. As part of the Munich Center for Machine Learning (MCML), the group combines sound theoretical approaches with practical applications, including in the areas of intelligent information systems and data-driven decision-making processes.

Current research topics

• Process mining
• Unsupervised learning
  • Clustering
  • Subspace clustering
  • Deep clustering

• Semi-supervised learning
  • Active learning
  • Few labels Learning

• Large vision-language learning

• Outlier detection
• Stream mining

Publications