Wouter Duivesteijn

785 total citations
25 papers, 311 citations indexed

About

Wouter Duivesteijn is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Wouter Duivesteijn has authored 25 papers receiving a total of 311 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 13 papers in Information Systems and 6 papers in Computational Theory and Mathematics. Recurrent topics in Wouter Duivesteijn's work include Data Mining Algorithms and Applications (13 papers), Rough Sets and Fuzzy Logic (6 papers) and Bayesian Modeling and Causal Inference (5 papers). Wouter Duivesteijn is often cited by papers focused on Data Mining Algorithms and Applications (13 papers), Rough Sets and Fuzzy Logic (6 papers) and Bayesian Modeling and Causal Inference (5 papers). Wouter Duivesteijn collaborates with scholars based in Netherlands, Germany and Belgium. Wouter Duivesteijn's co-authors include Arno Knobbe, Joaquin Vanschoren, Ad Feelders, Peter J. F. M. Lohuis, Abel‐Jan Tasman, Gregor Bran, Arno Siebes, Antti Ukkonen, Mykola Pechenizkiy and Carlos Soares and has published in prestigious journals such as PLoS ONE, IEEE Access and Plastic & Reconstructive Surgery.

In The Last Decade

Wouter Duivesteijn

21 papers receiving 301 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Wouter Duivesteijn Netherlands 9 138 105 78 52 49 25 311
David Boaz Israel 9 119 0.9× 33 0.3× 20 0.3× 59 1.1× 16 0.3× 19 258
Conghua Zhou China 10 79 0.6× 70 0.7× 22 0.3× 10 0.2× 16 0.3× 42 239
Parag Verma India 9 96 0.7× 38 0.4× 16 0.2× 50 1.0× 1 0.0× 31 335
Marco Postiglione Italy 10 112 0.8× 23 0.2× 16 0.2× 29 0.6× 7 0.1× 31 279
Ander de Keijzer Netherlands 10 133 1.0× 70 0.7× 128 1.6× 122 2.3× 8 0.2× 28 390
Zhishu Li China 9 47 0.3× 42 0.4× 9 0.1× 13 0.3× 9 0.2× 85 270
Mengyue Liu China 7 140 1.0× 66 0.6× 10 0.1× 15 0.3× 11 0.2× 25 264
Felix Gräßer Germany 8 206 1.5× 57 0.5× 14 0.2× 25 0.5× 32 0.7× 13 369

Countries citing papers authored by Wouter Duivesteijn

Since Specialization
Citations

This map shows the geographic impact of Wouter Duivesteijn's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Wouter Duivesteijn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wouter Duivesteijn more than expected).

Fields of papers citing papers by Wouter Duivesteijn

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Wouter Duivesteijn. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Wouter Duivesteijn. The network helps show where Wouter Duivesteijn may publish in the future.

Co-authorship network of co-authors of Wouter Duivesteijn

This figure shows the co-authorship network connecting the top 25 collaborators of Wouter Duivesteijn. A scholar is included among the top collaborators of Wouter Duivesteijn based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Wouter Duivesteijn. Wouter Duivesteijn is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Duivesteijn, Wouter, et al.. (2021). Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions. Data Mining and Knowledge Discovery. 36(1). 379–413. 2 indexed citations
3.
Pei, Yulong, et al.. (2020). Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling. Data Mining and Knowledge Discovery. 34(5). 1267–1290. 2 indexed citations
4.
Luna, José María, Mykola Pechenizkiy, Wouter Duivesteijn, & Sebastián Ventura. (2020). Exceptional in so Many Ways—Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios. IEEE Access. 8. 200982–200994.
5.
Duivesteijn, Wouter, Sibylle Hess, & Xin Du. (2020). How to cheat the page limit. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 10(3).
6.
Duivesteijn, Wouter, et al.. (2018). ELBA: Exceptional Learning Behavior Analysis.. TU/e Research Portal. 312–318. 4 indexed citations
7.
Sá, Cláudio Rebelo de, Wouter Duivesteijn, Paulo J. Azevedo, et al.. (2018). Discovering a taste for the unusual: exceptional models for preference mining. Machine Learning. 107(11). 1775–1807. 10 indexed citations
8.
Duivesteijn, Wouter, et al.. (2017). Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, Technische Universiteit Eindhoven, 9-10 June 2017. TU/e Research Portal (Eindhoven University of Technology). 17(6). 614–21.
9.
Duivesteijn, Wouter, et al.. (2016). Exceptionally monotone models—the rank correlation model class for Exceptional Model Mining. Knowledge and Information Systems. 51(2). 369–394. 4 indexed citations
10.
Duivesteijn, Wouter, et al.. (2015). Exceptionally Monotone Models -- The Rank Correlation Model Class for Exceptional Model Mining. Ghent University Academic Bibliography (Ghent University). 21. 111–120. 3 indexed citations
11.
Duivesteijn, Wouter, Ad Feelders, & Arno Knobbe. (2015). Exceptional Model Mining. Data Mining and Knowledge Discovery. 30(1). 47–98. 43 indexed citations
12.
Duivesteijn, Wouter. (2014). A short survey of exceptional model mining: exploring unusual interactions between multiple targets. Ghent University Academic Bibliography (Ghent University). 1 indexed citations
13.
Duivesteijn, Wouter, et al.. (2014). ROCsearch — An ROC-guided Search Strategy for Subgroup Discovery. Ghent University Academic Bibliography (Ghent University). 704–712. 5 indexed citations
14.
Duivesteijn, Wouter, et al.. (2014). Understanding Where Your Classifier Does (Not) Work -- The SCaPE Model Class for EMM. 809–814. 8 indexed citations
15.
Lohuis, Peter J. F. M., et al.. (2013). Benefits of a Short, Practical Questionnaire to Measure Subjective Perception of Nasal Appearance after Aesthetic Rhinoplasty. Plastic & Reconstructive Surgery. 132(6). 913e–923e. 52 indexed citations
16.
Lohuis, Peter J. F. M., et al.. (2012). Split Hump Technique for Reduction of the Overprojected Nasal Dorsum. Archives of Facial Plastic Surgery. 14(5). 346–346. 17 indexed citations
17.
Lohuis, Peter J. F. M., et al.. (2012). Split Hump Technique for Reduction of the Overprojected Nasal Dorsum. Archives of Facial Plastic Surgery. 14(5). 346–353. 11 indexed citations
18.
Duivesteijn, Wouter, Ad Feelders, & Arno Knobbe. (2012). Different slopes for different folks. 868–876. 16 indexed citations
19.
Duivesteijn, Wouter & Arno Knobbe. (2011). Exploiting False Discoveries -- Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. 151–160. 27 indexed citations
20.
Duivesteijn, Wouter, et al.. (2010). Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. 158–167. 26 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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