Patterns
that Matter

News and updates

  • 01.09.2016 NEW JOB! I am now assistant professor Data Science at the Leiden Institute of Advanced Computer Science (LIACS).
  • 28.06.2016 Our paper titled Expect the Unexpected - On the Significance of Subgroups, with Antti Ukkonen, got accepted at DS 2016.
  • 01.06.2016 Hugo Proença has started as a PhD student in the SAPPAO project, in collaboration with IIT Roorkee and GE Aviation. He will work on pattern mining for flight data. Welcome Hugo!
  • 30.05.2016 Our paper titled Simultaneous discovery of cancer subtypes and subtype features by molecular data integration, with Thanh Le Van et al., got accepted at Bioinformatics. Congratulations Thanh!
  • 01.05.2016 Sander van Rijn has started as a PhD student in the DAMIOSO project, in collaboration with Honda Research. He will work on simulation data mining. Welcome Sander!
  • 10.03.2016 IDEA 2016, our (full-day!) workshop on Interactive Data Exploration and Analytics, got accepted at KDD 2016.
  • 02.12.2015 Due to popular demand, the submission deadline for the TKDD special issue on Interactive Data Exploration and Analytics has been extended to Dec 11, 2015 (final!).
  • 30.10.2015 Our paper titled A KNIME-based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules, with Daniëlle Copmans, Thorsten Meinl, Christian Dietz, Julia Ortmann, Michael Berthold, and Peter de Witte, got accepted at JBS.
  • 19.10.2015 Our paper titled Subjective Interestingness of Subgraph Patterns, with Tijl De Bie, Eirini Spyropoulou, and Cedric Mesnage, got accepted for publication in Machine Learning.
  • 11.09.2015 I will be Workshop and Tutorial Co-Chair of ECML PKDD 2016, which will take place in Riva del Garda, Italy, September 19-23, 2016.

I am assistant professor Data Science at the Leiden Institute of Advanced Computer Science (LIACS) at Leiden University, where I participate in the Leiden Data Science research programme. My main interest is exploratory data mining: how can we enable domain experts to explore and analyse their data, to discover structure and ultimately novel knowledge?

The approach I take 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. I often use pattern-based modelling techniques, for which information theoretic concepts such as the Minimum Description Length (MDL) principle has proven very useful. I am also interested in interactive data mining, i.e., involving humans in the loop.

Finally, I find it very interesting to do fundamental data mining for real-world applications, both in science (e.g., life sciences, social sciences) and industry (e.g., manufacturing and engineering, aviation). There is no better way to show the potential of exploratory data mining than by demonstrating that patterns matter.


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Activities

Current and upcoming
  • Publicity Co-ChairSDM 2017.
  • Guest editorTKDD special issue on Interactive Data Exploration and Analytics.
Recent

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Selected recent publications

In press
Le Van, T., van Leeuwen, M., Fierro, A.C., 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. In: Bioinformatics, vol.?(?), ?.
van Leeuwen, M., De Bie, T., Spyropoulou, E. & Mesnage, C. Subjective Interestingness of Subgraph Patterns. In: Machine Learning, vol.?(?), ?.
2016
Chau, D.H., Vreeken, J., van Leeuwen, M., Shahaf, D. & Faloutsos, C. (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA 2016), 2016.
van Leeuwen, M., & Ukkonen, A. Expect the Unexpected - On the Significance of Subgroups. In: Proceedings of Discovery Science (DS'16), 2016.
van Leeuwen, M. & Galbrun, E. Association Discovery in Two-View Data (extended abstract). In: TKDE Poster Track of ICDE 2016, 2016.
Copmans, D., Meinl, T., Dietz, C., van Leeuwen, M., Ortmann, J., Berthold, M.R. & de Witte, P.A.M. A KNIME-based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules. In: Journal of Biomolecular Screening, vol.21(5), 2016.
2015
van Leeuwen, M. & Galbrun, E. Association Discovery in Two-View Data. In: Transactions on Knowledge and Data Engineering, vol.27(12), 2015.
Fromont, E., De Bie, T. & van Leeuwen, M. (eds) Advances in Intelligent Data Analysis XIV (proceedings of IDA 2015), LNCS 9385, Springer, 2015.
Aksehirli, E., Nijssen, S., van Leeuwen, M. & Goethals, B. Finding Subspace Clusters using Ranked Neighborhoods. In: Workshop proceedings of ICDM 2015 (HDM workshop), 2015.
Chau, D.H., Vreeken, J., van Leeuwen, M., Shahaf, D. & Faloutsos, C. (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA 2015), 2015.
van Leeuwen, M. & Cardinaels, L. VIPER - Visual Pattern Explorer. Demo paper at: ECML PKDD 2015, 2015.
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), 2015.
Le Van, Th., 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), 2015.
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), 2015.
2014
Dzyuba, V., van Leeuwen, M., Nijssen, S. & De Raedt, L. Interactive Learning of Pattern Rankings. In: International Journal on Artificial Intelligence Tools, vol.23(6), 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: Frequent Pattern Mining (Aggarwal, C.C., & Han, J., eds), Springer, 2014.
van Leeuwen, M. & Ukkonen, A. Fast Estimation of the Pattern Frequency Spectrum. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2014 (ECML PKDD'14), 2014.
Le Van, Th., van Leeuwen, M., Nijssen, S., Fierro, A.C., Marchal, K. & De Raedt, L. Ranked Tiling. In: Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data 2014 (ECML PKDD'14), 2014.
Chau, D.H., Vreeken, J., van Leeuwen, M. & Faloutsos, C. (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA 2014), 2014.
van Leeuwen, M. Interactive Data Exploration using Pattern Mining. In: Interactive Knowledge Discovery and Data Mining: State-of-the-Art and Future Challenges in Biomedical Informatics (Holzinger, A. & Jurisica, I., eds), LNCS, Springer, 2014.
Pool, S., Bonchi, F. & van Leeuwen, M. Description-driven Community Detection. In: Transactions on Intelligent Systems and Technology, vol.5(2), ACM, 2014.