Organization profile

Introduction / mission

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.

Organisational 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.

Success stories

We have created OpenML: an online collaborative platform for studying machine learning techniques. OpenML is used by almost 2,000 researchers, students, and practitioners world-wide, and contains around 20,000 datasets, 3,000 machine learning workflows, and 1,7 million shared experiments. It has won the Dutch Data Prize, as well as backing from Microsoft Research. It is crucial for the development of automated machine learning that is adopted by companies such as Philips.

Further information at OpenML.org

Project examples

NWO RATE-Analytics (with Tilburg University, Rabobank and Achmea) "Next generation predictive analytics for data-driven banking and insurance".
ImpulseKYC-Analytics (with Rabobank) "Know your customer predictive analytics" project aims at developing approaches for effective DM on heterogeneous and evolving data sources with expert-in-the-loop.
STW CAPA (with Adversitement and StudyPortals)"Context-aware predictive analytics" advanced the current state of the art in Web analytics.
NWO Veni "Detection methods for similarity structures in time-dependent data"develops foundations for advanced time series and trajectories clustering.
H2020 SODA (ICT-2016-1; Big Data PPP) "Scalable Oblivious Data Analytics" facilitates secure DM; together with Crypto group we develop practical approaches for DM with multi-party computation.

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Profiles

Anne Driemel

Department of Mathematics and Computer Science, Data Mining

Person: OWP : University Teacher / Researcher

Wouter Duivesteijn

w.duivesteijn@tue.nl

Department of Mathematics and Computer Science, Data Mining
Department of Mathematics and Computer Science, Information Systems WSK&I - Assistant Professor
Department of Mathematics and Computer Science, Data Mining - Assistant Professor

Person: UD : Assistant Professor

V. Menkovski

v.menkovski@tue.nl

Department of Mathematics and Computer Science, Information Systems WSK&I - Assistant Professor
Department of Mathematics and Computer Science, Data Mining
Department of Mathematics and Computer Science, Data Mining - Assistant Professor

Person: UD : Assistant Professor

Projects

1 Finished

Interoperability of Heterogeneous IoT Platforms

Exarchakos, G., Mocanu, D. C. & Exarchakos, G.

1/01/16 → 31/12/18

Project: Research direct

Research Output

Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning

Järvelä, S., Gašević, D., Seppänen, T., Pechenizkiy, M. & Kirschner, P. A., 1 Jan 2020, (Accepted/In press) In : British Journal of Educational Technology.

Research output: Contribution to journal › Article › Academic › peer-review