Explanatory Data Analysis group

Publications

Publications by year (by type)

In press

Kroes, SKS, Janssen, MP, Groenwold, RHH & van Leeuwen, M Evaluating privacy of individuals in medical data. Health Informatics Journal, SAGE Publications

2021

Marx, A, Yang, L & van Leeuwen, M Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. In: Proceedings of the SIAM Conference on Data Mining 2021 (SDM'21), SIAM, 2021.website
Kapoor, S, Saxena, DK & van Leeuwen, M Online Summarization of Dynamic Graphs using Subjective Interestingness for Sequential Data. Data Mining and Knowledge Discovery vol.35(1), pp 88-126, 2021. (ECML PKDD journal track)

2020

Gawehns, D & van Leeuwen, M Social Fluidity in Children's Face-to-Face Interaction Networks. In: Proceedings of the Graph Embedding and Mining (GEM) Workshop at ECML PKDD 2020, 2020.website
Proença, HM, Grünwald, P, Bäck, T & van Leeuwen, M Discovering Outstanding Subgroup Lists for Numeric Targets using MDL. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2020), Springer, 2020.implementationwebsite
van Rijn, S & Schmitt, S MF2: A Collection of Multi-Fidelity Benchmark Functions in Python. Journal of Open Source Software vol.5(52), 2020.
Vinkenoog, M, van den Hurk, K, van Kraaij, M, van Leeuwen, M & Janssen, M First results of a ferritin-based blood donor deferral policy in the Netherlands. Transfusion vol.60(8), pp 1785-1792, Wiley, 2020.
Faas, M & van Leeuwen, M Vouw: Geometric Pattern Mining using the MDL Principle. In: Proceedings of the Eighteenth International Symposium on Intelligent Data Analysis (IDA 2020), Springer, 2020.
Gautrais, C, Cellier, P, van Leeuwen, M & Termier, A Widening for MDL-based Retail Signature Discovery. In: Proceedings of the Eighteenth International Symposium on Intelligent Data Analysis (IDA 2020), Springer, 2020.
Kapoor, S, Saxena, DK & van Leeuwen, M Discovering Subjectively Interesting Multigraph Patterns. Machine Learning, pp 1-28, Springer
Proença, HM & van Leeuwen, M Interpretable multiclass classification by MDL-based rule lists. Information Sciences vol.512, pp 1372-1393, Elsevier, 2020.implementationwebsite

2019

Vinkenoog, M, Janssen, M & van Leeuwen, M Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories. In: Proceedings of 4th Workshop on Advanced Analytics and Learning on Temporal Data at ECMLPKDD 2019, Springer, 2019.
Gawehns, D, Veiga, G & van Leeuwen, M Focus on dynamics: a proof of principle in exploratory data mining of face-to-face interactions. In: Proceedings of the 5th International Conference on Computational Social Science (IC2S2), 2019. (Poster presentation)
van Leeuwen, M, Chau, DH, Vreeken, J, Shahaf, D & Faloutsos, C Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12, Iss. 1): Introduction by the Guest Editors. Transactions on Knowledge Discovery from Data vol.13(1), ACM, 2019.

2018

Proença, HM, Klijn, R, Bäck, T & van Leeuwen, M Identifying flight delay patterns using diverse subgroup discovery. In: Proceedings of the Symposium Series on Computational Intelligence (SSCI'18), IEEE, 2018.
van Os, H, Ramos, L, Hilbert, A, van Leeuwen, M, van Walderveen, M, Kruyt, N, Dippel, D, Steyerberg, E, van der Schaaf, I, Lingsma, H, Schonewille, W, Majoie, C, Olabarriaga, S, Zwinderman, K, Venema, E, Marquering, H & Wermer, M Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms. Frontiers in Neurology vol.9(784), Frontiers, 2018.
van Rijn, S, Doerr, C & Bäck, T Towards an Adaptive CMA-ES Configurator. In: Proceedings of the International Conference on Parallel Problem Solving from Nature (PPSN'18), 2018.
Doerr, C, Ye, F, van Rijn, S, Wang, H & Bäck, T Towards a Theory-Guided Benchmarking Suite for Discrete Black-Box Optimization: Profiling (1+λ) EA Variants on OneMax and LeadingOnes. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'18), pp 951-958, ACM, 2018.
van Rijn, S, van Leeuwen, M, Schmitt, S, Olhofer, M & Bäck, T Multi-Fidelity Surrogate Model Approach to Optimization. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'18), ACM, 2018.
van Leeuwen, M, Chau, DH, Vreeken, J, Shahaf, D & Faloutsos, C Editorial: TKDD Special Issue on Interactive Data Exploration and Analytics. Transactions on Knowledge Discovery from Data vol.12(1), ACM, 2018.

2017

Paramonov, S, van Leeuwen, M & De Raedt, L Relational Data Factorization. Machine Learning vol.106(12), pp 1867-1904, Springer, 2017.
Ukkonen, A, Dzyuba, V & van Leeuwen, M Explaining Deviating Subsets through Explanation Networks. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'17), Springer, 2017.
Dzyuba, V, van Leeuwen, M & De Raedt, L Flexible constrained sampling with guarantees for pattern mining. Data Mining and Knowledge Discovery vol.31(5), pp 1266-1293, Springer, 2017. (ECMLPKDD'17 Special Issue)implementation
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'17). 2017.website
Le Van, T, Nijssen, S, van Leeuwen, M & De Raedt, L Semiring Rank Matrix Factorisation. Transactions on Knowledge and Data Engineering vol.29(8), pp 1737-1750, IEEE, 2017.
van Rijn, S, Wang, H, van Stein, B & Bäck, T Algorithm configuration data mining for CMA evolution strategies. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'17), pp 737-744, ACM, 2017.
Dzyuba, V & van Leeuwen, M Learning what matters – Sampling interesting patterns. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'17), pp 534-546, Springer, 2017.

2016

van Stein, B, van Leeuwen, M, Wang, H, Purr, S, Kreissl, S, Meinhardt, J & Bäck, T Towards Data Driven Process Control in Manufacturing Car Body Parts. In: Proceedings of IEEE International Conference on Computational Science and Computational Intelligence (IEEE CSCI-ISBD'16), IEEE, 2016.
van Rijn, S, Wang, H, van Leeuwen, M & Bäck, T Evolving the Structure of Evolution Strategies. In: Proceedings of IEEE Symposium Series on Computational Intelligence (IEEE SSCI'16), IEEE, 2016.
van Stein, B, van Leeuwen, M & Bäck, T Local Subspace-Based Outlier Detection using Global Neighbourhoods. In: Proceedings of IEEE International Conference on Big Data (IEEE BigData'16), IEEE, 2016.
van Leeuwen, M & Ukkonen, A Expect the Unexpected - On the Significance of Subgroups. In: Proceedings of Discovery Science (DS'16), pp 51-66, Springer, 2016.
Le Van, T, van Leeuwen, M, Fierro, AC, De Maeyer, D, Van den Eynden, J, Verbeke, L, De Raedt, L, Marchal, K & Nijssen, S Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinformatics vol.32(17), pp 445-454, Oxford University Press, 2016.implementation
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'16). 2016.website
Copmans, D, Meinl, T, Dietz, C, van Leeuwen, M, Ortmann, J, Berthold, M & de Witte, PAM A KNIME-based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules. Journal of Biomolecular Screening vol.21(5), pp 427-436, SAGE Publishing, 2016.implementation
van Leeuwen, M & Galbrun, E Association Discovery in Two-View Data (extended abstract). In: TKDE Poster Track of ICDE 2016, IEEE, 2016.implementation
van Leeuwen, M, De Bie, T, Spyropoulou, E & Mesnage, C Subjective Interestingness of Subgraph Patterns. Machine Learning vol.105(1), pp 41-75, Springer, 2016.implementation

2015

Aksehirli, E, Nijssen, S, van Leeuwen, M & Goethals, B Finding Subspace Clusters using Ranked Neighborhoods. In: Proceedings of the ICDM 2015 workshops (HDM workshop), IEEE, 2015.
Fromont, E, De Bie, T & van Leeuwen, M (eds) Advances in Intelligent Data Analysis XIV (proceedings of IDA 2015). LNCS 9385, Springer, 2015.
van Leeuwen, M & Cardinaels, L VIPER - Visual Pattern Explorer. Demo paper in: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'15), Springer, 2015.
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'15). 2015.website
van Leeuwen, M & Galbrun, E Association Discovery in Two-View Data. Transactions on Knowledge and Data Engineering vol.27(12), pp 3190-3202, IEEE, 2015.implementation
Paramonov, S, van Leeuwen, M, Denecker, M & De Raedt, L An exercise in declarative modeling for relational query mining. In: Proceedings of the 25th International Conference On Inductive Logic Programming (ILP'15), pp 166-182, Springer, 2015.
Le Van, T, van Leeuwen, M, Nijssen, S & De Raedt, L Rank Matrix Factorisation. In: Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'15), pp 734-746, Springer, 2015.implementation
van Leeuwen, M & Ukkonen, A Same bang, fewer bucks: efficient discovery of the cost-influence skyline. In: Proceedings of the SIAM Conference on Data Mining 2015 (SDM'15), SIAM, 2015.implementation
van Leeuwen, M, Siebes, A & Vreeken, J Information Theoretic Methods in Data Mining. At the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'14), Nancy, France, 2015.website

2014

Dzyuba, V, van Leeuwen, M, Nijssen, S & De Raedt, L Interactive Learning of Pattern Rankings. International Journal on Artificial Intelligence Tools vol.23(6), World Scientific, 2014.
Blockeel, H, van Leeuwen, M & Vinciotti, V (eds) Advances in Intelligent Data Analysis XIII (proceedings of IDA 2014). LNCS 8819, Springer, 2014.
van Leeuwen, M & Vreeken, J Mining and Using Sets of Patterns through Compression. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 165-198, Springer, 2014.
van Leeuwen, M & Ukkonen, A Estimating the pattern frequency spectrum inside the browser. Technical Report arXiv:1409.7311, arXiv, 2014.implementation
van Leeuwen, M & Ukkonen, A Fast Estimation of the Pattern Frequency Spectrum. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'14), pp 114-129, Springer, 2014.implementation
Le Van, T, van Leeuwen, M, Nijssen, S, Fierro, AC, Marchal, K & De Raedt, L Ranked Tiling. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'14), pp 98-113, Springer, 2014.
Siebes, A, van Leeuwen, M & Vreeken, J Information Theoretic Methods in Data Mining. At the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'14), Nancy, France, 2014.website
Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'14). 2014.website
Pool, S, Bonchi, F & van Leeuwen, M Description-driven Community Detection. Transactions on Intelligent Systems and Technology vol.5(2), ACM, 2014.implementation
van Leeuwen, M Interactive Data Exploration using Pattern Mining. In: Holzinger, A & Jurisica, I (eds) Interactive Knowledge Discovery and Data Mining: State-of-the-Art and Future Challenges in Biomedical Informatics, LNCS 8401, Springer, 2014.

2013

Dzyuba, V, van Leeuwen, M, Nijssen, S & De Raedt, L Active Preference Learning for Ranking Patterns. In: Proceedings of the International Conference on Tools with Artificial Intelligence 2013 (ICTAI'13), IEEE, 2013. (Best Paper)
Dzyuba, V & van Leeuwen, M Interactive Discovery of Interesting Subgroup Sets. In: Proceedings of the Twelfth International Symposium on Intelligent Data Analysis (IDA 2013), pp 150-161, Springer, 2013.
van Leeuwen, M & Ukkonen, A Discovering Skylines of Subgroup Sets. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13), pp 272-287, Springer, 2013.
De Raedt, L, Paramonov, S & van Leeuwen, M Relational Decomposition using Answer Set Programming. In: LNMR'13 - 1st Workshop on Learning and Non-Monotonic Reasoning (at LPNMR), 2013.
Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA'13). ACM, 2013.website

2012

Le Van, T, Fierro, C, Guns, T, van Leeuwen, M, Nijssen, S, De Raedt, L & Marchal, K Mining Local Staircase Patterns in Noisy Data. In: Proceedings of the ICDM 2012 workshops (CoClus workshop), IEEE, 2012.
van Leeuwen, M & Puspitaningrum, D Improving Tag Recommendation using Few Associations. In: Proceedings of the Eleventh International Symposium on Intelligent Data Analysis Data 2012 (IDA 2012), pp 184-194, Springer, 2012.
Vreeken, J, van Leeuwen, M, Nijssen, S, Tatti, N, Dries, A & Goethals, B (eds) Proceedings of the ECML PKDD Workshop on Instant Interactive Data Mining (IID'12). 2012.website
van Leeuwen, M & Knobbe, AJ Diverse Subgroup Set Discovery. Data Mining and Knowledge Discovery vol.25(2), pp 208-242, Springer, 2012. (ECMLPKDD'11 Special Issue)implementation

2011

van Leeuwen, M & Knobbe, AJ Non-Redundant Subgroup Discovery in Large and Complex Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2011 (ECML PKDD'11), pp 459-474, Springer, 2011.
Bonchi, F, van Leeuwen, M & Ukkonen, A Characterizing Uncertain Data using Compression. In: Proceedings of the SIAM Conference on Data Mining 2011 (SDM'11), SIAM, 2011.
Vreeken, J, van Leeuwen, M & Siebes, A Krimp: Mining Itemsets that Compress. Data Mining and Knowledge Discovery vol.23(1), pp 169-214, Springer, 2011.implementation

2010

Duivesteijn, W, Knobbe, AJ, Feelders, A & van Leeuwen, M Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10), IEEE, 2010.
van Leeuwen, M Maximal Exceptions with Minimal Descriptions. Data Mining and Knowledge Discovery vol.21(2), pp 259-276, Springer, 2010. (ECMLPKDD'10 Special Issue)
van Leeuwen, M Patterns that Matter. Dissertation, Universiteit Utrecht, 2010.

2009

van Leeuwen, M, Bonchi, F, Sigurbjörnsson, B & Siebes, A Compressing Tags to Find Interesting Media Groups. In: Proceedings of the ACM Conference on Information and Knowledge Management (CIKM'09), pp 1147-1156, ACM, 2009. (Runner-up Best Student Paper)
van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. Data Mining and Knowledge Discovery vol.19(2), pp 176-193, Springer, 2009. (ECMLPKDD'09 Special Issue) (Best Student Paper)video
van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'09), pp 32-32, Springer, 2009. (ECMLPKDD'09 Best Student Paper)video

2008

van Leeuwen, M & Siebes, A StreamKrimp: Detecting Change in Data Streams. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD'08), pp 672-687, LNCS 5211, Springer, 2008.implementation

2007

Vreeken, J, van Leeuwen, M & Siebes, A Preserving Privacy through Data Generation. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'07), pp 685-690, IEEE, 2007.
Siebes, A, van Leeuwen, M & Vreeken, J MDL for Pattern Mining. In: Proceedings of the International Conference on Statistics for Data Mining, Learning and Knowledge Extraction Models (IASC'07), 2007.
Vreeken, J, van Leeuwen, M & Siebes, A Characterising the Difference. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2007 (KDD'07), pp 765-774, ACM, 2007.
Vreeken, J, van Leeuwen, M & Siebes, A Privacy Preservation through Data Generation. Technical Report UU-CS-2007-020, Universiteit Utrecht, 2007.

2006

van Leeuwen, M, Vreeken, J & Siebes, A Compression Picks the Item Sets that Matter. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'06), pp 585-592, Springer, 2006.implementation
Siebes, A, Vreeken, J & van Leeuwen, M Item Sets That Compress. In: Proceedings of the SIAM International Conference on Data Mining (SDM'06), pp 393-404, SIAM, 2006.implementation
van Leeuwen, M, Vreeken, J & Siebes, A Compression Picks the Significant Item Sets. Technical Report UU-CS-2006-050, Universiteit Utrecht, 2006.implementation

2004

van Leeuwen, M Spike Timing Dependent Structural Plasticity, in a single model neuron. M.Sc. Thesis, Universiteit Utrecht, 2004.

2003

Koopman, ACM, van Leeuwen, M & Vreeken, J Exploring Temporal Memory of LSTM and Spiking Circuits. In: Workshop on the Future of Neural Networks (FUNN'03), 2003.

2002

van Leeuwen, M Evolutionary blimp & i. Technical Report (internship), Universiteit Utrecht, 2002.
Zufferey, J-C, Floreano, D, van Leeuwen, M & Merenda, T Evolving Vision-based Flying Robots. In: Proceedings of the 2nd International Workshop on Biologically Motivated Computer Vision 2002 (BMCV'02), 2002.