Universiteit Leiden

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Matthijs van Leeuwen

Assistant professor

Naam
Dr. M. van Leeuwen
Telefoon
+31 71 527 7048
E-mail
m.van.leeuwen@liacs.leidenuniv.nl

My main 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?

Meer informatie over Matthijs van Leeuwen

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. Moreover, I find it very interesting to do fundamental data mining for real-world applications; there is no better way to show the potential of exploratory data mining than by demonstrating that patterns matter.

For more information, see my personal website www.patternsthatmatter.org

Assistant professor

  • Wiskunde en Natuurwetenschappen
  • Leiden Inst Advanced Computer Sciences

Werkadres

Snellius
Niels Bohrweg 1
2333 CA Leiden
Kamernummer 148

Contact

  • Dzyuba V & van Leeuwen M (2017), Learning what matters - Sampling interesting patterns, arXiv preprint arXiv:1702.01975 .artikel in tijdschrift
  • Chau P, Vreeken J, van Leeuwen M, Shahaf D & Faloutsos C (2016), Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics. [overig]overig
  • van Leeuwen M, De Bie T, Spyropoulou E & Mesnage C (2016), Subjective interestingness of subgraph patterns, Machine Learning 105(1): 41--75.artikel in tijdschrift (refereed)
  • Le Van T, van Leeuwen M, Fierro AC, De Maeyer D, Van den Eynden J, Verbeke L, De Raedt L, Marchal K & Nijssen S (2016), Simultaneous discovery of cancer subtypes and subtype features by molecular data integration, Bioinformatics 32(17): i445--i454.artikel in tijdschrift (refereed)
  • Copmans D., Meinl T., Dietz C., Leeuwen M. van, Ortmann J., Berthold M.R. & Witte P.A. de (2016), A KNIME-Based Analysis of the Zebrafish Photomotor Response Clusters the Phenotypes of 14 Classes of Neuroactive Molecules, Journal of biomolecular screening 21(5): 427-436.artikel in tijdschrift (refereed)
  • Dzyuba V., Leeuwen M. van & Raedt L. de (2016), Flexible constrained sampling with guarantees for pattern mining, arXiv preprint arXiv:1610.09263 .artikel in tijdschrift
  • Sander van Rijn, Wang H., Leeuwen M. van & Bäck T.H.W. (2016), Evolving the Structure of Evolution Strategies.congresbijdrage (refereed)
  • van Stein B., van Leeuwen M. & Bäck T.H.W. (2016), Local Subspace-Based Outlier Detection using Global Neighbourhoods: Springer.congresbijdrage
  • van Stein B., van Leeuwen M., Wang H., Purr S., Kreissl S., Meinhardt J. & Bäck T.H.W. (2016), Towards Data Driven Process Control in Manufacturing Car Body Parts: IEEE CPS.congresbijdrage
  • Leeuwen M. van & Galbrun E. (2015), Association Discovery in Two-View Data, Transactions on Knowledge and Data Engineering 27(12): 3190 - 3202.artikel in tijdschrift (refereed)
  • Chau P, Vreeken J, van Leeuwen M & Faloutsos C (2015), Proceedings of the ACM SIGKDD 2015 Full-day Workshop on Interactive Data Exploration and Analytics. [overig]overig
  • Van T. Le, Leeuwen M. van, Nijssen S.G.R. & Raedt L. De (2015), Rank Matrix Factorisation. In: Proceedings Advances in Knowledge Discovery and Data Mining - 19th Pacific-Asia Conference, PAKDD 2015, volume I. nr. LNCS 9077. 734-746.congresbijdrage (refereed)
  • van Leeuwen M & Ukkonen A (2015), Same bang, fewer bucks: efficient discovery of the cost-influence skyline. In: Proceedings of the 2015 SIAM International Conference on Data Mining.. 19--27.congresbijdrage (refereed)
  • Fromont E., De Bie T., Leeuwen & M. van (red.) (2015), Advances in Intelligent Data Analysis XIV Lecture notes in Computer Science nr. 9385: Springer.boekredactie
  • Leeuwen M. van & Cardinaels L. (2015), VIPER - Visual Pattern Explorer. In: Bifet A., May M., Zadrozny B., Gavalda R., Pedreschi D., Bonchi F., Cardoso J., Spiliopoulou M. (red.) Machine Learning and Knowledge Discovery in Databases. nr. 9286: Springer. 333-336.congresbijdrage (refereed)
  • Aksehirli E., Nijssen S.G.R., Leeuwen M. van & Goethals B. (2015), Finding subspace clusters using ranked neighborhoods. In: 2015 IEEE International Conference on Data Mining Workshop (ICDMW).: IEEE Publishing. 831-838.congresbijdrage (refereed)
  • Paramonov S., van Leeuwen M., Denecker M. & De Raedt L. (2015), An exercise in declarative modeling for relational query mining.congresbijdrage (refereed)
  • Beyers J & Braun C (2014), Ties that Count. Explaining interest group access to policy makers. In: International Conference on Discovery Science.. 93-121.congresbijdrage (refereed)

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