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 

  • 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.

Fingerprint Dive into the research topics where Data Mining is active. These topic labels come from the works of this organisation's members. Together they form a unique fingerprint.

Learning systems Engineering & Materials Science
Data mining Engineering & Materials Science
Classifiers Engineering & Materials Science
Hypermedia systems Engineering & Materials Science
Video streaming Engineering & Materials Science
Students Engineering & Materials Science
Feedback Engineering & Materials Science
Experiments Engineering & Materials Science

Network Recent external collaboration on country level. Dive into details by clicking on the dots.

Projects 2016 2018

Interoperability of Heterogeneous IoT Platforms

Exarchakos, G., Mocanu, D. C., van der Lee, T. & Exarchakos, G.


Project: Research direct

Research Output 2006 2020

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling

Du, X., Pei, Y., Duivesteijn, W. & Pechenizkiy, M., 2020, (Accepted/In press) In : Data Mining and Knowledge Discovery.

Research output: Contribution to journalArticleAcademicpeer-review

Fairness in network representation by latent structural heterogeneity in observational data

Du, X., Pei, Y., Duivesteijn, W. & Pechenizkiy, M., 2020, (Accepted/In press) Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

On local and global graph structure mining

Pei, Y., 5 Feb 2020, (Accepted/In press) Eindhoven: Technische Universiteit Eindhoven. 198 p.

Research output: ThesisPhd Thesis 1 (Research TU/e / Graduation TU/e)

Open Access


Third Prize - National Olympiad in Informatics

Decebal C. Mocanu (Recipient), 1997

Prize: OtherDiscipline relatedScientific

Activities 2016 2018

  • 3 Invited talk
  • 1 Keynote talk
  • 1 Visiting an external academic institution

Machine Learning, better, together.

Joaquin Vanschoren (Speaker)
8 Dec 2018

Activity: Talk or presentation typesInvited talkScientific

Tutorial on Automatic Machine Learning

Frank Hutter (Speaker), Joaquin Vanschoren (Speaker)
3 Dec 2018

Activity: Talk or presentation typesKeynote talkScientific

Democratizing and Automating Machine Learning

Joaquin Vanschoren (Speaker)
28 Aug 2018

Activity: Talk or presentation typesInvited talkScientific

Press / Media

4. The internet will continue to make life better

Joaquin Vanschoren


1 item of Media coverage

Press/Media: Expert Comment

Data correlation helps recognize pickpockets

Mykola Pechenizkiy


2 items of Media coverage

Press/Media: Expert Comment

Conference Interpretation Today

Mykola Pechenizkiy


1 item of Media coverage

Press/Media: Expert Comment

Student theses

A framework for understanding business process remaining time predictions

Author: Verhoef, C., 28 Oct 2019

Supervisor: Pechenizkiy, M. (Supervisor 1) & Scheepens, R. J. (Supervisor 2)

Student thesis: Master

Algorithms for center-based trajectory clustering

Author: van de L'Isle, N., 28 Jan 2019

Supervisor: Buchin, K. (Supervisor 1) & Driemel, A. (Supervisor 2)

Student thesis: Master


An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks

Author: Remmits, Y., 30 Sep 2019

Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach)

Student thesis: Master