self_20140107.jpg Email: wouter.duivesteijn At sign cs.uni-dortmund.de


Hi, I'm Wouter, and I'm incurably curious. Currently, I am employed as a postdoctoral researcher in the Collaborative Research Center SFB 876 at the Technische Universität Dortmund. Before that, I was employed as a PhD candidate in the Data Mining group of LIACS, Leiden University.

During my PhD candidacy, I have been working on the Exceptional Model Mining (EMM) project, funded by NWO. EMM is a framework that can be seen as a generalisation of Subgroup Discovery (SD). 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. In SD, `behaviour' is traditionally interpreted in terms of the distribution of a single nominal variable. EMM, on the other hand, seeks subgroups for which a fitted local model is surprisingly different from a global model. In this approach, `behaviour' is described by a number of attributes, and fitting a model captures multivariate dependencies between these attributes.

Currently, my research interests revolve around SD and EMM in a somewhat wider scope. On the one hand, I am interested in combining concepts from Subgroup Discovery and ROC Analysis. On the other hand, I am interested in SD and EMM on streams. 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. Eventually, though, I strive to develop a full-fledged algorithm that allows to mine for interesting subgroups directly on the data stream.


Downar/Duivesteijn/2015a Downar, Lennart and Duivesteijn, Wouter. Exceptionally Monotone Models - the Rank Correlation Model Class for Exceptional Model Mining. In Data Mining (ICDM), 2015 IEEE International Conference on, pages 111-120, IEEE, IEEE Computer Society, 2015.
Duivesteijn/2013a Duivesteijn, Wouter. Exceptional Model Mining. Leiden Institute of Advanced Computer Science (LIACS), Faculty of Science, Leiden University, 2013.
Konijn/etal/2013a Rob M. Konijn and Wouter Duivesteijn and Marvin Meeng and Arno J. Knobbe. Cost-Based Quality Measures in Subgroup Discovery. In Jiuyong Li and Longbing Cao and Can Wang and Kay Chen Tan and Bo Liu and Jian Pei and Vincent S. Tseng (editors), PAKDD Workshops, Vol. 7867, pages 404-415, Springer, 2013.
Konijn/etal/2013b Rob M. Konijn and Wouter Duivesteijn and Wojtek Kowalczyk and Arno J. Knobbe. Discovering Local Subgroups, with an Application to Fraud Detection. In Jian Pei and Vincent S. Tseng and Longbing Cao and Hiroshi Motoda and Guandong Xu (editors), PAKDD (1), Vol. 7818, pages 1-12, Springer, 2013.
Duivesteijn/etal/2012a Wouter Duivesteijn and Ad Feelders and Arno J. Knobbe. Different slopes for different folks: mining for exceptional regression models with cook's distance. In Qiang Yang and Deepak Agarwal and Jian Pei (editors), KDD, pages 868-876, ACM, 2012.
Duivesteijn/etal/2012b Wouter Duivesteijn and Loza Menc\'ia, Eneldo and Johannes Fürnkranz and Arno J. Knobbe. Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. In Jaakko Hollmén and Frank Klawonn and Allan Tucker (editors), IDA, Vol. 7619, pages 114-125, Springer, 2012.
Ribeiro/etal/2012a Geraldina Ribeiro and Wouter Duivesteijn and Carlos Soares and Arno J. Knobbe. Multilayer Perceptron for Label Ranking. In Alessandro E. P. Villa and Wlodzislaw Duch and Péter Érdi and Francesco Masulli and Günther Palm (editors), ICANN (2), Vol. 7553, pages 25-32, Springer, 2012.
Duivesteijn/Knobbe/2011a Wouter Duivesteijn and Arno J. Knobbe. Exploiting False Discoveries - Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. In Diane J. Cook and Jian Pei and Wei Wang and Osmar R. Zaïane and Xindong Wu (editors), ICDM, pages 151-160, IEEE, 2011.
Duivesteijn/etal/2010a Wouter Duivesteijn and Arno J. Knobbe and Ad Feelders and Matthijs van Leeuwen. Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. In Geoffrey I. Webb and Bing Liu and Chengqi Zhang and Dimitrios Gunopulos and Xindong Wu (editors), ICDM, pages 158-167, IEEE Computer Society, 2010.
Duivesteijn/Feelders/2008a Wouter Duivesteijn and Ad Feelders. Nearest Neighbour Classification with Monotonicity Constraints. In Walter Daelemans and Bart Goethals and Katharina Morik (editors), ECML/PKDD (1), Vol. 5211, pages 301-316, Springer, 2008.

Supervised Theses

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