Nearest Neighbour Classification with Monotonicity Constraints. - Wouter Duivesteijn; Ad Feelders (ECML/PKDD (1), 2008)

Subgroup Discovery Meets Bayesian Networks – An Exceptional Model Mining Approach. - Wouter Duivesteijn; Arno J. Knobbe; Ad Feelders; Matthijs van Leeuwen (ICDM, 2010)

Exploiting False Discoveries - Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. - Wouter Duivesteijn; Arno J. Knobbe (ICDM, 2011)

Multilayer Perceptron for Label Ranking. - Geraldina Ribeiro; Wouter Duivesteijn; Carlos Soares; Arno J. Knobbe (ICANN (2), 2012)

Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. - Wouter Duivesteijn; Eneldo Loza Mencía; Johannes Fürnkranz; Arno J. Knobbe (IDA, 2012)

Different slopes for different folks: mining for exceptional regression models with cook's distance. - Wouter Duivesteijn; Ad Feelders; Arno J. Knobbe (KDD, 2012)

Discovering Local Subgroups, with an Application to Fraud Detection. - Rob M. Konijn; Wouter Duivesteijn; Wojtek Kowalczyk; Arno J. Knobbe (PAKDD (1), 2013)

Understanding Where Your Classifier Does (Not) Work - The SCaPE Model Class for EMM. - Wouter Duivesteijn; Julia Thaele (ICDM, 2014)

RealKrimp - Finding Hyperintervals that Compress with MDL for Real-Valued Data. - Jouke Witteveen; Wouter Duivesteijn; Arno J. Knobbe; Peter Grünwald (IDA, 2014)

ROCsearch - An ROC-guided Search Strategy for Subgroup Discovery. - Marvin Meeng; Wouter Duivesteijn; Arno J. Knobbe (SDM, 2014)

Understanding Where Your Classifier Does (Not) Work. - Wouter Duivesteijn; Julia Thaele (ECML/PKDD (3), 2015)

Cost-based quality measures in subgroup discovery. - Rob M. Konijn; Wouter Duivesteijn; Marvin Meeng; Arno J. Knobbe (J. Intell. Inf. Syst., 2015)

SCHEP - A Geometric Quality Measure for Regression Rule Sets, Gauging Ranking Consistency Throughout the Real-Valued Target Space. - Wouter Duivesteijn; Marvin Meeng (Solving Large Scale Learning Tasks, 2016)

Exceptional Preferences Mining. - Cláudio Rebelo de Sá; Wouter Duivesteijn; Carlos Soares; Arno J. Knobbe (DS, 2016)

Exceptional Model Mining - Supervised descriptive local pattern mining with complex target concepts. - Wouter Duivesteijn; Ad Feelders; Arno J. Knobbe (Data Min. Knowl. Discov., 2016)

Interpretable domain adaptation via optimization over the Stiefel manifold. - Christian Pölitz; Wouter Duivesteijn; Katharina Morik (Mach. Learn., 2016)

Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining. - Wouter Duivesteijn; Tara Farzami; Thijs Putman; Evertjan Peer; Hilde J. P. Weerts; Jasper N. Adegeest; Gerson Foks; Mykola Pechenizkiy (ECML/PKDD (3), 2017)

BoostEMM - Transparent Boosting using Exceptional Model Mining. - Simon van der Zon; Oren Zeev-Ben-Mordehai; Tom Vrijdag; Werner van Ipenburg; Wouter Duivesteijn; Jan Veldsink; Mykola Pechenizkiy (MIDAS@PKDD/ECML, 2017)

Exceptionally monotone models - the rank correlation model class for Exceptional Model Mining. - Lennart Downar; Wouter Duivesteijn (Knowl. Inf. Syst., 2017)

ELBA: Exceptional Learning Behavior Analysis. - Xin Du 006; Wouter Duivesteijn; Mykola Pechenizkiy (EDM, 2018)

Subjectively Interesting Subgroup Discovery on Real-Valued Targets. - Jefrey Lijffijt; Bo Kang; Wouter Duivesteijn; Kai Puolamäki; Emilia Oikarinen; Tijl De Bie (ICDE, 2018)

ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations. - Simon van der Zon; Wouter Duivesteijn; Werner van Ipenburg; Jan Veldsink; Mykola Pechenizkiy (MIDAS/PAP@PKDD/ECML, 2018)

Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally. - Oren Zeev-Ben-Mordehai; Wouter Duivesteijn; Mykola Pechenizkiy (CoRR, 2018)

Discovering a taste for the unusual: exceptional models for preference mining. - Cláudio Rebelo de Sá; Wouter Duivesteijn; Paulo J. Azevedo; Alípio Mário Jorge; Carlos Soares; Arno J. Knobbe (Mach. Learn., 2018)

The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. - Sibylle Hess; Wouter Duivesteijn; Philipp Honysz; Katharina Morik (AAAI, 2019)

DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups. - Adnene Belfodil; Wouter Duivesteijn; Marc Plantevit; Sylvie Cazalens; Philippe Lamarre (ECML/PKDD (1), 2019)

k Is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. - Sibylle Hess; Wouter Duivesteijn (ECML/PKDD (1), 2019)

Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data. - Xin Du 006; Yulong Pei; Wouter Duivesteijn; Mykola Pechenizkiy (AAAI, 2020)

Predicting Remaining Useful Life with Similarity-Based Priors. - Youri Soons; Remco M. Dijkman; Maurice Jilderda; Wouter Duivesteijn (IDA, 2020)

Exceptional in so Many Ways - Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios. - José María Luna; Mykola Pechenizkiy; Wouter Duivesteijn; Sebastián Ventura (IEEE Access, 2020)

Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring. - Sibylle Hess; Wouter Duivesteijn; Decebal Constantin Mocanu (CoRR, 2020)

Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling. - Xin Du 006; Yulong Pei; Wouter Duivesteijn; Mykola Pechenizkiy (Data Min. Knowl. Discov., 2020)

How to cheat the page limit. - Wouter Duivesteijn; Sibylle Hess; Xin Du 006 (WIREs Data Mining Knowl. Discov., 2020)

Exceptional Gestalt Mining: Combining Magic Cards to Make Complex Coalitions Thrive. - Wouter Duivesteijn; Thomas C. van Dijk (MLSA@PKDD/ECML, 2021)

Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles. - Xin Du 006; Subramanian Ramamoorthy; Wouter Duivesteijn; Jin Tian; Mykola Pechenizkiy (CoRR, 2021)

Adversarial balancing-based representation learning for causal effect inference with observational data. - Xin Du 006; Lei Sun; Wouter Duivesteijn; Alexander G. Nikolaev; Mykola Pechenizkiy (Data Min. Knowl. Discov., 2021)

A Clustering-Inspired Quality Measure for Exceptional Preferences Mining - Design Choices and Consequences. - Ruben Franciscus Adrianus Verhaegh; Jacco Johannes Egbert Kiezebrink; Frank Nusteling; Arnaud Wander André Rio; Márton Bendegúz Bendicsek; Wouter Duivesteijn; Rianne Margaretha Schouten (DS, 2022)

Efficient Subgroup Discovery Through Auto-Encoding. - Joost F. van der Haar; Sander C. Nagelkerken; Igor G. Smit; Kjell van Straaten; Janneke A. Tack; Rianne Margaretha Schouten; Wouter Duivesteijn (IDA, 2022)

Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). - Rianne Margaretha Schouten; Wouter Duivesteijn; Mykola Pechenizkiy (SDM, 2022)

Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions. - Rianne Margaretha Schouten; Marcos L. P. Bueno; Wouter Duivesteijn; Mykola Pechenizkiy (Data Min. Knowl. Discov., 2022)

RMI-RRG: A Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets. - Iko Vloothuis; Wouter Duivesteijn (IDA (1), 2024)

Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression. - Rianne Margaretha Schouten; Wouter Duivesteijn; Pekka Räsänen; Jacob M. Paul; Mykola Pechenizkiy (ECML/PKDD (10), 2024)

Local Subgroup Discovery on Attributed Network Graphs. - Carl Vico Heinrich; Tommie Lombarts; Jules Mallens; Luc Tortike; David Wolf; Wouter Duivesteijn (IDA, 2025)