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Personal profile


I am incurably curious!

Research profile

Wouter Duivesteijn is an Assistant Professor in Data Mining at the Technische Universiteit Eindhoven. Currently, his research interests revolve around SD and EMM in a wider scope. He is interested in combining concepts from Subgroup Discovery and ROC Analysis as well as SD and EMM on streams. Both SD and EMM attempt to find small portions of the data where the observed behaviour is notably different from that of the database as a whole. Initially this can take the relatively simple form of extracting a meaningful flat table representation of features from the stream, and observing the results that can be obtained by running out-of-the-box SD and EMM algorithms on that table. Wouter aims to eventually develop a full-fledged algorithm that allows to mine for interesting subgroups directly on the data stream.

Academic background

Wouter Duivesteijn obtained his PhD in Computer Science from Leiden University. He also holds MScs in Applied Computing Science and Mathematical Sciences from Utrecht University. Before joining TU/e, Wouter worked on the FORSIED project (FORmalising Subjective Interestingness in Exploratory Data mining) at the University of Ghent and the University of Bristol to. Before that, he worked as a Wissenschaftlicher Mitarbeiter at the Collaborative Research Center SFB 876 at the Technische Universität Dortmund and at the Data Mining group of LIACS, Leiden University.

Wouter is chairman of the board of student association A–Eskwadraat for Mathematics, Physics, Computer Science and Information Science, as well as a member of the science faculty council, mathematics department council, mathematics department board of education and mathematics education council at Utrecht University. He has acted as Conference chair of Benelearn 2017 and workshop chair of Silver 2012, collocated with ECML PKDD in Bristol, UK. He has been a program committee member of a wide range of events and reviewed for numerous leading journals.

Fingerprint Dive into the research topics where Wouter Duivesteijn is active. These topic labels come from the works of this person. Together they form a unique fingerprint.

Adaptive boosting Engineering & Materials Science
Mining Mathematics
Interpretability Mathematics
Data mining Engineering & Materials Science
Clustering algorithms Engineering & Materials Science
Labels Engineering & Materials Science
Ensemble Mathematics
Students Engineering & Materials Science

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

Research Output 2017 2020

5 Citations (Scopus)

Exceptionally monotone models : the rank correlation model class for Exceptional Model Mining

Downar, L. & Duivesteijn, W., 2017, In : Knowledge and Information Systems. 51, 2, p. 369-394

Research output: Contribution to journalArticleAcademicpeer-review

Computational complexity

BoostEMM : Transparent boosting using exceptional model mining

van der Zon, S. B., Zeev Ben Mordehay, O., Vrijdag, T. S., van Ipenburg, W., Veldsink, J., Duivesteijn, W. & Pechenizkiy, M., 2017, Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia . Bordino, I., Caldarelli, G., Fumarola, F., Gullo, F. & Squartini, T. (eds.). p. 5-16 12 p. (CEUR Workshop Proceedings; vol. 1941).

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

Open Access
Adaptive boosting

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

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

Deep learning


Foundations of data mining

1/09/17 → …


Responsible Data Science

1/09/19 → …


Web analytics



Press / Media

2nd international workshop on learning over multiple contexts (LMCE 2015)

W. Duivesteijn


1 item of Media coverage

Press/Media: Expert Comment

Student theses

Analysis and improvement of process models with respect to key performance indicators: a debt collection case study

Author: Syring, A., 28 May 2018

Supervisor: de Leoni, M. (Supervisor 1), Schouten, M. (External person) (External coach), Duivesteijn, W. (Supervisor 2) & Türetken, O. (Supervisor 2)

Student thesis: Master


Association rule mining of student grades: a grammar guided genetic programming approach

Author: Giesbers, B., 31 Oct 2016

Supervisor: Pechenizkiy, M. (Supervisor 1), Serebrenik, A. (Supervisor 2) & Duivesteijn, W. (Supervisor 2)

Student thesis: Master


Exceptional incidence distribution mining on a nationwide cancer registry: a descriptive approach

Author: Attanasio, C., 16 Dec 2019

Supervisor: Duivesteijn, W. (Supervisor 1) & Buisman, H. J. (External coach)

Student thesis: Master


Exceptional model mining of convolutional neural networks

Author: van Strien, B., 29 Apr 2019

Supervisor: Duivesteijn, W. (Supervisor 1)

Student thesis: Master


Mining exceptional descriptive patterns in attributed spatio-temporal datasets

Author: Scheerder, M., 29 Apr 2019

Supervisor: Duivesteijn, W. (Supervisor 1), Duin, P. (External person) (External coach) & de Jonge, E. (External person) (External coach)

Student thesis: Master