Krimp: mining itemsets that compress J Vreeken, M Van Leeuwen, A Siebes Data Mining and Knowledge Discovery 23, 169-214, 2011 | 389 | 2011 |
Item Sets That Compress A Siebes, J Vreeken, M van Leeuwen Proceedings of the Sixth SIAM International Conference on Data Mining 124, 395, 2006 | 208 | 2006 |
Diverse subgroup set discovery M Van Leeuwen, A Knobbe Data Mining and Knowledge Discovery 25, 208-242, 2012 | 164 | 2012 |
Predicting outcome of endovascular treatment for acute ischemic stroke: potential value of machine learning algorithms HJA Van Os, LA Ramos, A Hilbert, M Van Leeuwen, ... Frontiers in neurology 9, 784, 2018 | 140 | 2018 |
Description-driven community detection S Pool, F Bonchi, M Leeuwen ACM Transactions on Intelligent Systems and Technology (TIST) 5 (2), 1-28, 2014 | 97 | 2014 |
StreamKrimp: Detecting Change in Data Streams M Van Leeuwen, A Siebes Machine Learning and Knowledge Discovery in Databases: European Conference …, 2008 | 91 | 2008 |
Interactive data exploration using pattern mining M Van Leeuwen Interactive knowledge discovery and data mining in biomedical informatics …, 2014 | 88 | 2014 |
Compression picks item sets that matter M van Leeuwen, J Vreeken, A Siebes Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on …, 2006 | 87 | 2006 |
Subgroup discovery meets bayesian networks--an exceptional model mining approach W Duivesteijn, A Knobbe, A Feelders, M van Leeuwen 2010 IEEE International Conference on Data Mining, 158-167, 2010 | 81 | 2010 |
Characterising the difference J Vreeken, M Van Leeuwen, A Siebes Proceedings of the 13th ACM SIGKDD international conference on Knowledge …, 2007 | 81 | 2007 |
Evolving vision-based flying robots JC Zufferey, D Floreano, M Van Leeuwen, T Merenda Biologically Motivated Computer Vision: Second International Workshop, BMCV …, 2002 | 76 | 2002 |
Evolving the structure of evolution strategies S van Rijn, H Wang, M van Leeuwen, T Bäck 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-8, 2016 | 63 | 2016 |
Non-redundant subgroup discovery in large and complex data M Van Leeuwen, A Knobbe Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 56 | 2011 |
Interpretable multiclass classification by MDL-based rule lists HM Proença, M van Leeuwen Information Sciences 512, 1372-1393, 2020 | 52 | 2020 |
Subjective interestingness of subgraph patterns M van Leeuwen, T De Bie, E Spyropoulou, C Mesnage Machine Learning 105, 41-75, 2016 | 46 | 2016 |
Local subspace-based outlier detection using global neighbourhoods B Van Stein, M Van Leeuwen, T Bäck 2016 IEEE International Conference on Big Data (Big Data), 1136-1142, 2016 | 45 | 2016 |
Maximal exceptions with minimal descriptions M van Leeuwen Data Mining and Knowledge Discovery 21, 259-276, 2010 | 44 | 2010 |
Interactive discovery of interesting subgroup sets V Dzyuba, M van Leeuwen Advances in Intelligent Data Analysis XII: 12th International Symposium, IDA …, 2013 | 43 | 2013 |
A survey on explainable anomaly detection Z Li, Y Zhu, M Van Leeuwen ACM Transactions on Knowledge Discovery from Data 18 (1), 1-54, 2023 | 42 | 2023 |
Flexible constrained sampling with guarantees for pattern mining V Dzyuba, M van Leeuwen, L De Raedt Data Mining and Knowledge Discovery 31, 1266-1293, 2017 | 41 | 2017 |