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

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

Person: UD : Assistant Professor

Vlado 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

Person: UD : Assistant Professor

Projects 2016 2018

1 Finished

Interoperability of Heterogeneous IoT Platforms

Liotta, A., Exarchakos, G., van Hout, J., Mocanu, D. C., van der Lee, T., van Mil, J., van Mil, J. & Exarchakos, G.

1/01/16 → 31/12/18

Project: Research direct

Research Output 2006 2019

Adversarial balancing-based representation learning for causal effect inference with observational data

Du, X., Sun, L., Duivesteijn, W., Nikolaev, A. & Pechenizkiy, M., 30 Apr 2019, In : arXiv. 17 p., 1904.13335v1.

Research output: Contribution to journal › Article › Academic

Open Access

File

Education

Experiments

Deep learning

24 Downloads (Pure)

A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers

Mantovani, R. G., Rossi, A. L. D., Alcobaça, E., Vanschoren, J. & de Carvalho, A. C. P. L. F., 1 Oct 2019, In : Information Sciences. 501, p. 193-221 29 p.

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

Open Access

File

Meta-learning

Hyperparameters

Recommender Systems

Recommender systems

Learning Systems

1 Downloads (Pure)

Anomaly detection for visual quality control of 3D-printed products

Tonnaer, L., Li, J., Osin, V., Holenderski, M. & Menkovski, V., 1 Jul 2019, 2019 International Joint Conference on Neural Networks, IJCNN 2019. Piscataway: Institute of Electrical and Electronics Engineers, 8 p. 8852372

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Quality control

Defects

Surface defects

Image resolution