Cluster Artificial Intelligence

Data Mining

The chair studies data mining (DM) techniques and knowledge discovery approaches that are at the core of data science. The group is known for its contributions to the areas of predictive analytics, automation of machine learning and networked science, subgroup discovery and exceptional model mining, and similarity computations on complex data. Its research is inspired by theoretical computer science, systems development and real-world applications of (big) data-driven discovery in healthcare, banking, energy, retail, telecom, and education among others.

Research Profile

We develop generic approaches and specialized techniques that cover a wide range of descriptive, predictive and prescriptive analytics and work effectively with text, image, transactional, graph and time-series data in a responsible manner. E.g. we use Deep Learning methods to develop models for high dimensional heterogeneous, unstructured and evolving data and apply this models to areas such as medical imaging, genomics, anomaly detection and sentiment analysis. We further work on methods for analyzing and explaining the model’s decisions and performance and facilitate effective DM with domain expert in the loop.

Meet some of our Researchers

Full Professor

Mykola Pechenizkiy

Former Doctoral Candidate

Simon van der Zon

Associate Professor

Jakub Tomczak

Postdoc

Shiwei Liu

Assistant Professor

Vlado Menkovski

Doctoral Candidate

Ricky Fajri

Assistant Professor

Wouter Duivesteijn

Assistant Professor

Bahram Zonooz

Assistant Professor

Decebal Mocanu

University Researcher

Elahe Arani

Associate Professor

Joaquin Vanschoren

Recent Publications

Our most recent peer reviewed publications

Education