Patterns
that Matter

News and updates

  • 14.09.2022 Our paper titled Generating synthetic mixed discrete-continuous health records with mixed sum-product networks, with Shannon Kroes, Rolf Groenwold, and Mart Janssen got accepted for publication in the Journal of the American Medical Informatics Association. Congratulations Shannon!
  • 01.09.2022 I am now Director of Education of LIACS' Master's programmes, and in that role also a member of the institute's management team.
  • 01.09.2022 Francesco Bariatti has joined the EDA group as a postdoctoral researcher. Welcome Francesco!
  • 14.08.2022 Our paper titled Explainable hemoglobin deferral predictions using machine learning models: interpretation and consequences for the blood supply, with Marieke Vinkenoog et al. got accepted for publication in Vox Sanguinis. Congratulations Marieke!
  • 22.07.2022 Our paper titled Feature Selection for Fault Detection and Prediction based on Log Analysis, with Zhong Li, got accepted at the AI for manufacturing workshop at ECML PKDD 2022. Congratulations Zhong!
  • 21.07.2022 Our paper titled Histogram-based Probabilistic Rule Lists for Numeric Targets, with Lincen Yang and Tim Opdam, got accepted at the KDID 2022 workshop at ECML PKDD 2022. Congratulations Lincen and Tim!
  • 14.06.2022 Our paper titled Truly Unordered Probabilistic Rule Sets for Multi-class Classification, with Lincen Yang, got accepted at ECML PKDD 2022. Congratulations Lincen!
  • 14.03.2022 Our paper titled Robust Subgroup Discovery, with Hugo Proença, Peter Grünwald, and Thomas Bäck, got accepted for publication in Data Mining and Knowledge Discovery. Congratulations Hugo!
  • 03.02.2022 Our paper titled Associations between symptoms, donor characteristics and IgG antibody response in 2082 COVID-19 convalescent plasma donors, with Marieke Vinkenoog et al. got accepted for publication in Frontiers in Immunology. Congratulations Marieke!
  • 27.01.2022 I received the Senior Teaching Qualification (SKO) certificate! Read the news article.
  • 20.01.2022 Our paper titled Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling, with Sander van Rijn, Sebastian Schmitt, and Thomas Bäck got accepted for publication in Engineering Optimization. Congratulations Sander!
  • 16.10.2021 Hugo Manuel Proença has successfully defended his PhD thesis titled Robust rules for prediction and description. Congratulations Dr. Hugo Proença!
  • 14.06.2021 Sarang Kapoor has successfully defended his PhD thesis titled Subjectively Interesting Patterns in Networks. Congratulations Dr. Sarang Kapoor!

I am associate professor and director of education at the Leiden Institute of Advanced Computer Science (LIACS), the computer science institute of Leiden University. I am group leader of the Explanatory Data Analysis group.

My primary research interest is exploratory data mining: how can we enable domain experts to explore and analyse their data, to discover structure and—ultimately—novel knowledge?

For this it is important that methods and results are explainable to domain experts, who may not be data scientists. My signature approach is to define and identify patterns that matter, i.e., succinct descriptions that characterise relevant structure present in the data. Which patterns matter strongly depends on the data and task at hand, hence defining the problem is one of the key challenges of exploratory data mining. Information theoretic concepts such as the Minimum Description Length (MDL) principle have proven very useful to this end. I am also interested in interactive data mining, i.e., involving humans in the loop. Finally, I am interested in fundamental data mining research for real-world applications, both in science (e.g., life sciences, social sciences) and industry (e.g., manufacturing and engineering, aviation), as this is the best way to show that the theory works in practice.

I am affiliated with SAILS, the university-wide research programme for artificial intelligence. Broadly speaking, my research can be situated in the fields of data mining, machine learning, data science, and artificial intelligence (AI).

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Selected recent publications

2022
Kroes, SKS, van Leeuwen, M, Groenwold, RHH & Janssen, MP Generating synthetic mixed discrete-continuous health records with mixed sum-product networks. Journal of the American Medical Informatics Association , Oxford University Press
Spaink, HA, Verhagen, IE, van Leeuwen, M & Terwindt, GM Methodological considerations in predicting migraine attacks using machine learning. In: MTIS 2022 Cephalalgia Abstracts, Sage Publications, 2022.
Li, Z & van Leeuwen, M Feature Selection for Fault Detection and Prediction based on Log Analysis. In: Proceedings of the international workshop on AI for Manufacturing Workshop at ECMLPKDD 2022, 2022.
Yang, L, Opdam, T & van Leeuwen, M Histogram-based Probabilistic Rule Lists for Numeric Targets. In: Proceedings of the international workshop on Knowledge Discovery in Inductive Databases (KDID 2022) at ECMLPKDD 2022, 2022.
Yang, L & van Leeuwen, M Truly Unordered Probabilistic Rule Sets for Multi-class Classification. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2022), Springer, 2022.implementationwebsite
Vinkenoog, M, van Leeuwen, M & Janssen, M Explainable hemoglobin deferral predictions using machine learning models: interpretation and consequences for the blood supply. Vox Sanguinis
Proença, HM, Grünwald, P, Bäck, T & van Leeuwen, M Robust subgroup discovery - Discovering subgroup lists using MDL. Data Mining and Knowledge Discovery
implementationwebsite
van Rijn, S, Schmitt, S, van Leeuwen, M & Bäck, T Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling. Engineering Optimization
website
Yang, L & van Leeuwen, M Probabilistic Rule Sets Ready for Interactive Machine Learning. In: AAAI'22-Workshop on Interactive Machine Learning, 2022.
Vinkenoog, M, Steenhuis, M, ten Brinke, A, van Hasselt, C, Janssen, M, van Leeuwen, M, Swaneveld, F, Vrielink, H, van de Watering, L, Quee, F, van cen Hurk, K, Rispens, T, Hogema, B & van der Schoot, E Associations between symptoms, donor characteristics and IgG antibody response in 2082 COVID-19 convalescent plasma donors. Frontiers in Immunology, Frontiers
2021
Kroes, SKS, Janssen, MP, Groenwold, RHH & van Leeuwen, M Evaluating privacy of individuals in medical data. Health Informatics Journal, SAGE Publications
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)
implementation
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
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