Projects per year
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.
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.
Profiles
Wouter Duivesteijn
- Department of Mathematics and Computer Science, Information Systems WSK&I - Assistant Professor
- Department of Mathematics and Computer Science, Data Mining
Person: UD : Assistant Professor
Vlado Menkovski
- Department of Mathematics and Computer Science, Information Systems WSK&I - Assistant Professor
- Department of Mathematics and Computer Science, Data Mining
Person: UD : Assistant Professor
Projects 2016 2018
- 1 Finished
Interoperability of Heterogeneous IoT Platforms
Exarchakos, G., Mocanu, D. C., van der Lee, T. & Exarchakos, G.
1/01/16 → 31/12/18
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 journal › Article › Academic › peer-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 proceeding › Conference contribution › Academic › peer-review
On local and global graph structure mining
Pei, Y., 5 Feb 2020, (Accepted/In press) Eindhoven: Technische Universiteit Eindhoven. 198 p.Research output: Thesis › Phd Thesis 1 (Research TU/e / Graduation TU/e)
Open Access
File
Prizes
Third Prize - National Olympiad in Informatics
Decebal C. Mocanu (Recipient), 1997
Prize: Other › Discipline related › Scientific
Activities 2016 2018
Machine Learning, better, together.
Joaquin Vanschoren (Speaker)8 Dec 2018
Activity: Talk or presentation types › Invited talk › Scientific
Tutorial on Automatic Machine Learning
Frank Hutter (Speaker), Joaquin Vanschoren (Speaker)3 Dec 2018
Activity: Talk or presentation types › Keynote talk › Scientific
Democratizing and Automating Machine Learning
Joaquin Vanschoren (Speaker)28 Aug 2018
Activity: Talk or presentation types › Invited talk › Scientific
Press / Media
4. The internet will continue to make life better
28/10/19
1 item of Media coverage
Press/Media: Expert Comment
Data correlation helps recognize pickpockets
12/07/18
2 items of Media coverage
Press/Media: Expert Comment
Student theses
A framework for understanding business process remaining time predictions
Author: Verhoef, C., 28 Oct 2019Supervisor: 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 2019Supervisor: Buchin, K. (Supervisor 1) & Driemel, A. (Supervisor 2)
Student thesis: Master
File
An exploration and evaluation of concept based interpretability methods as a measure of representation quality in neural networks
Author: Remmits, Y., 30 Sep 2019Supervisor: Menkovski, V. (Supervisor 1) & Stolikj, M. (External coach)
Student thesis: Master
File