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Personal profile

Quote

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 Fingerprint is based on mining the text of the person's scientific documents to create an index of weighted terms, which defines the key subjects of each individual researcher.

Adaptive boosting Engineering & Materials Science
Interpretability Mathematics
Ensemble Mathematics
Testing Engineering & Materials Science
Estimator Mathematics
Websites Engineering & Materials Science
Industry Engineering & Materials Science
Profitability Engineering & Materials Science

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Research Output 2017 2019

3 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) (CEUR Workshop Proceedings).

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

Adaptive boosting
Transparency

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

30th Benelux conference on artificial intelligence: BNAIC 2018 preproceedings, November 8-9, 2018, 's-Hertogenbosch, The Netherlands

Atzmüller, M. (ed.) & Duivesteijn, W. (ed.), 2018, 344 p.

Research output: Book/ReportBook editingAcademicpeer-review

Courses

Data analytics for engineers

1/09/17 → …

Course

Foundations of data mining

1/09/17 → …

Course

Web analytics

1/09/13 → …

Course

Press / Media

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

File

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

File

Exceptional model mining of convolutional neural networks

Author: van Strien, B., 29 Apr 2019

Supervisor: Duivesteijn, W. (Supervisor 1)

Student thesis: Master

File

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

File

Regression models on bivariate count data in an exceptional model mining context

Author: Raaijmakers, B. A., 31 Aug 2017

Supervisor: De Andrade Serra, P. (Supervisor 1) & Duivesteijn, W. (Supervisor 2)

Student thesis: Bachelor

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