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 

  • 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.

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

Projects 2016 2018

Interoperability of Heterogeneous IoT Platforms

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

1/01/1631/12/18

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 journalArticleAcademic

Open Access
File
Education
Experiments
Deep learning

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 journalArticleAcademicpeer-review

Open Access
File
Meta-learning
Hyperparameters
Recommender Systems
Recommender systems
Learning Systems
1 Citation (Scopus)

Approximating (k,ℓ)-center clustering for curves

Buchin, K., Driemel, A., Gudmundsson, J., Horton, M., Kostitsyna, I., Löffler, M. & Struijs, M., 2 Jan 2019, 30th Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). Chan, T. M. (ed.). Society for Industrial and Applied Mathematics (SIAM), p. 2922-2938 17 p.

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

Open Access
Clustering
Curve
Simplification
Approximation
Hardness of Approximation

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

Student theses

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

File

Automated machine learning with gradient boosting and meta-learning

Author: van Hoof, J., 28 Jan 2019

Supervisor: Vanschoren, J. (Supervisor 1)

Student thesis: Master

File

Automatic data cleaning

Author: Zhang, J., 17 Dec 2018

Supervisor: Vanschoren, J. (Supervisor 1)

Student thesis: Master

File