Leiden Institute of Advanced Computer Science

Wouter Duivesteijn

Contents of this website:
Contact information
Research
Subset of educational tasks
Other stuff

Contact information

Name:	Wouter Duivesteijn
Postal address:	LIACS
	Niels Bohrweg 1
	2333 CA Leiden
Room:	113	(*)
Tel.:	+31 71 527 8927	(**)
Fax:	+31 71 527 6985
E-mail:	wouterd@liacs.nl	(***)

(*) usually in the office on Mondays, Tuesdays and Fridays, but this is just a rule of thumb.
(**) if I do not respond, I might be in my office in Utrecht. You can try +31 30 253 9274 to find out.
(***) please do not send me MS Word documents. If my antiword script can extract the content I will read it, but I will not enjoy it.

Research

For years I claimed to be a mathematician who also happened to do computer science. After receiving my Master's degrees with a final thesis on a data mining topic, I finally converted to computer science in 2009, when I started as a Ph.D. student in the Data Mining group of LIACS. My mathematical background is quite helpful in theoretical computer science research, and the moments when this is the most apparent are the ones that make doing research fun.

I am working on the NWO Exceptional Model Mining (EMM) project. 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. But, whereas in SD `behaviour' is traditionally interpreted in terms of the distribution of a single nominal variable, EMM seeks subgroups for which the fitted local model is surprisingly different from the global model. In this approach, `behaviour' is described by a number of attributes, and fitting a model captures the multivariate dependencies between these attributes.

List of publications

2011
- Duivesteijn, W., Knobbe, A.: Exploiting False Discoveries - Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. In: Proc. ICDM 2011, pp. 151-160.
  Acceptance rate: 0.1285 (101 out of 786). Including short papers: 0.1883 (148 out of 786).
2010
- Duivesteijn, W., Knobbe, A., Feelders, A., van Leeuwen, M.: Subgroup Discovery meets Bayesian networks - an Exceptional Model Mining approach. In: Proc. ICDM 2010, pp. 158-167.
  Acceptance rate: 0.0903 (72 out of 797). Including short papers: 0.1945 (155 out of 797).
- Hakim, S., Knobbe, A., Duivesteijn, W., Lohuis, P.J.F.M.: Results of a screening questionnaire measuring physical perception of patients undergoing esthetic rhinoplasty: a statistical analysis. In: Dutch Journal for Otorhinolaryngology (2), pp. 100, 2010.
2008
- Duivesteijn, W., Feelders, A.: Nearest Neighbour Classification with Monotonicity Constraints. In: Proc. ECML PKDD 2008 (I), pp. 301-316, 2008.
  Acceptance rate: 0.1919 (100 out of 521).

Subset of educational tasks

Fundamentele Informatica 3, spring 2012 (in Dutch).
Fundamentele Informatica 3, spring 2011 (in Dutch).
Algoritmiek, spring 2010 (in Dutch).
Concepten van Programmeertalen, fall 2009 (in Dutch).

Other stuff

The university logo in a format that prevents pixelation on posters.

Some thoughts on Dutch vowels.