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Matthijs van Leeuwen
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2020 – today
- 2024
- [j24]Zhong Li
, Yuxuan Zhu
A Survey on Explainable Anomaly Detection. ACM Trans. Knowl. Discov. Data 18(1): 23:1-23:54 (2024) - [j23]Zhong Li
Cross-Domain Graph Level Anomaly Detection. IEEE Trans. Knowl. Data Eng. 36(12): 7839-7850 (2024) - [c42]Zhong Li
Graph Neural Networks based Log Anomaly Detection and Explanation. ICSE Companion 2024: 306-307 - [i22]Lincen Yang, Matthijs van Leeuwen:
Probabilistic Truly Unordered Rule Sets. CoRR abs/2401.09918 (2024) - [i21]Zhong Li, Simon Geisler, Yuhang Wang, Stephan Günnemann, Matthijs van Leeuwen:
Explainable Graph Neural Networks Under Fire. CoRR abs/2406.06417 (2024) - [i20]Lincen Yang, Matthijs van Leeuwen:
Conditional Density Estimation with Histogram Trees. CoRR abs/2410.11449 (2024) - [i19]Oliver Urs Lenz, Matthijs van Leeuwen:
Directional anomaly detection. CoRR abs/2410.23158 (2024) - 2023
- [j22]Zhong Li
Explainable contextual anomaly detection using quantile regression forests. Data Min. Knowl. Discov. 37(6): 2517-2563 (2023) - [j21]Shannon K. S. Kroes
Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis. J. Cybersecur. Priv. 3(4): 882-894 (2023) - [j20]Lincen Yang
Unsupervised discretization by two-dimensional MDL-based histogram. Mach. Learn. 112(7): 2397-2431 (2023) - [c41]Antonio Lopez-Martinez-Carrasco
Novel Approach for Phenotyping Based on Diverse Top-K Subgroup Lists. AIME 2023: 45-50 - [c40]Antonio Lopez-Martinez-Carrasco
Discovering Diverse Top-K Characteristic Lists. IDA 2023: 262-273 - [c39]Ioanna Papagianni
Discovering Rule Lists with Preferred Variables. IDA 2023: 340-352 - [i18]Zhong Li, Matthijs van Leeuwen:
Robust and Explainable Contextual Anomaly Detection using Quantile Regression Forests. CoRR abs/2302.11239 (2023) - [i17]Richard van Dijk, Daniela Gawehns, Matthijs van Leeuwen:
WEARDA: recording wearable sensor data for human activity monitoring. CoRR abs/2303.00064 (2023) - [i16]Zhong Li, Jiayang Shi, Matthijs van Leeuwen:
Graph Neural Network based Log Anomaly Detection and Explanation. CoRR abs/2307.00527 (2023) - 2022
- [j19]Hugo Manuel Proença
Robust subgroup discovery. Data Min. Knowl. Discov. 36(5): 1885-1970 (2022) - [j18]Shannon K. S. Kroes
Generating synthetic mixed discrete-continuous health records with mixed sum-product networks. J. Am. Medical Informatics Assoc. 30(1): 16-25 (2022) - [j17]Zhong Li
Feature Selection for Fault Detection and Prediction based on Event Log Analysis. SIGKDD Explor. 24(2): 96-104 (2022) - [c38]Lincen Yang, Tim Opdam, Matthijs van Leeuwen:
Histogram-based Probabilistic Rule Lists for Numeric Targets (short paper). KDID 2022: 17-22 - [c37]Lincen Yang
Truly Unordered Probabilistic Rule Sets for Multi-class Classification. ECML/PKDD (5) 2022: 87-103 - [d2]Sander van Rijn
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling: Generated data files and figures. Version v2. Zenodo, 2022 [all versions] - [i15]Lincen Yang, Matthijs van Leeuwen:
Truly Unordered Probabilistic Rule Sets for Multi-class Classification. CoRR abs/2206.08804 (2022) - [i14]Zhong Li, Matthijs van Leeuwen:
Feature Selection for Fault Detection and Prediction based on Event Log Analysis. CoRR abs/2208.09440 (2022) - [i13]Zhong Li, Yuxuan Zhu, Matthijs van Leeuwen:
A Survey on Explainable Anomaly Detection. CoRR abs/2210.06959 (2022) - 2021
- [j16]Sarang Kapoor
Online summarization of dynamic graphs using subjective interestingness for sequential data. Data Min. Knowl. Discov. 35(1): 88-126 (2021) - [c36]Alexander Marx, Lincen Yang, Matthijs van Leeuwen
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. SDM 2021: 387-395 - [d1]Sander van Rijn
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling: Generated data files and figures. Version v1. Zenodo, 2021 [all versions] - [i12]Alexander Marx, Lincen Yang, Matthijs van Leeuwen:
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms. CoRR abs/2101.05009 (2021) - [i11]Sander van Rijn, Sebastian Schmitt, Matthijs van Leeuwen, Thomas Bäck:
Finding Efficient Trade-offs in Multi-Fidelity Response Surface Modeling. CoRR abs/2103.03280 (2021) - [i10]Hugo Manuel Proença
Robust subgroup discovery. CoRR abs/2103.13686 (2021) - 2020
- [j15]Hugo Manuel Proença
Interpretable multiclass classification by MDL-based rule lists. Inf. Sci. 512: 1372-1393 (2020) - [j14]Sarang Kapoor
Discovering subjectively interesting multigraph patterns. Mach. Learn. 109(8): 1669-1696 (2020) - [c35]Micky Faas, Matthijs van Leeuwen
Vouw: Geometric Pattern Mining Using the MDL Principle. IDA 2020: 158-170 - [c34]Clément Gautrais
Widening for MDL-Based Retail Signature Discovery. IDA 2020: 197-209 - [c33]Hugo Manuel Proença
Discovering Outstanding Subgroup Lists for Numeric Targets Using MDL. ECML/PKDD (1) 2020: 19-35 - [i9]Lincen Yang, Mitra Baratchi, Matthijs van Leeuwen:
Unsupervised Discretization by Two-dimensional MDL-based Histogram. CoRR abs/2006.01893 (2020) - [i8]Hugo Manuel Proença
Discovering outstanding subgroup lists for numeric targets using MDL. CoRR abs/2006.09186 (2020)
2010 – 2019
- 2019
- [j13]Matthijs van Leeuwen
Addendum to the Special Issue on Interactive Data Exploration and Analytics (TKDD, Vol. 12 Iss. 1). ACM Trans. Knowl. Discov. Data 13(1): 13:1-13:2 (2019) - [c32]Marieke Vinkenoog
Challenges and Limitations in Clustering Blood Donor Hemoglobin Trajectories. AALTD@PKDD/ECML 2019: 72-84 - [i7]Hugo Manuel Proença
Interpretable multiclass classification by MDL-based rule lists. CoRR abs/1905.00328 (2019) - [i6]Micky Faas, Matthijs van Leeuwen:
Vouw: Geometric Pattern Mining using the MDL Principle. CoRR abs/1911.09587 (2019) - 2018
- [c31]Sander van Rijn
Multi-fidelity surrogate model approach to optimization. GECCO (Companion) 2018: 225-226 - [c30]Hugo Manuel Proença
Identifying flight delay patterns using diverse subgroup discovery. SSCI 2018: 60-67 - 2017
- [j12]Vladimir Dzyuba
Flexible constrained sampling with guarantees for pattern mining. Data Min. Knowl. Discov. 31(5): 1266-1293 (2017) - [j11]Sergey Paramonov
Relational data factorization. Mach. Learn. 106(12): 1867-1904 (2017) - [j10]Thanh Le Van, Siegfried Nijssen
Semiring Rank Matrix Factorization. IEEE Trans. Knowl. Data Eng. 29(8): 1737-1750 (2017) - [c29]Vladimir Dzyuba, Matthijs van Leeuwen:
Learning What Matters - Sampling Interesting Patterns. PAKDD (1) 2017: 534-546 - [c28]Antti Ukkonen
Explaining Deviating Subsets Through Explanation Networks. ECML/PKDD (2) 2017: 425-441 - [i5]Vladimir Dzyuba, Matthijs van Leeuwen:
Learning what matters - Sampling interesting patterns. CoRR abs/1702.01975 (2017) - 2016
- [j9]Thanh Le Van, Matthijs van Leeuwen, Ana Carolina Fierro, Dries De Maeyer, Jimmy Van den Eynden
Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinform. 32(17): 445-454 (2016) - [j8]Matthijs van Leeuwen, Tijl De Bie, Eirini Spyropoulou, Cédric Mesnage:
Subjective interestingness of subgraph patterns. Mach. Learn. 105(1): 41-75 (2016) - [c27]Bas van Stein
Local subspace-based outlier detection using global neighbourhoods. IEEE BigData 2016: 1136-1142 - [c26]Matthijs van Leeuwen, Antti Ukkonen:
Expect the Unexpected - On the Significance of Subgroups. DS 2016: 51-66 - [c25]Matthijs van Leeuwen, Esther Galbrun:
Association discovery in two-view data. ICDE 2016: 1480-1481 - [c24]Sander van Rijn
Evolving the structure of Evolution Strategies. SSCI 2016: 1-8 - [i4]Sander van Rijn, Hao Wang, Matthijs van Leeuwen, Thomas Bäck:
Evolving the Structure of Evolution Strategies. CoRR abs/1610.05231 (2016) - [i3]Vladimir Dzyuba, Matthijs van Leeuwen, Luc De Raedt:
Flexible constrained sampling with guarantees for pattern mining. CoRR abs/1610.09263 (2016) - [i2]Bas van Stein, Matthijs van Leeuwen, Thomas Bäck:
Local Subspace-Based Outlier Detection using Global Neighbourhoods. CoRR abs/1611.00183 (2016) - 2015
- [j7]Matthijs van Leeuwen, Esther Galbrun:
Association Discovery in Two-View Data. IEEE Trans. Knowl. Data Eng. 27(12): 3190-3202 (2015) - [c23]Emin Aksehirli, Siegfried Nijssen, Matthijs van Leeuwen, Bart Goethals
Finding Subspace Clusters Using Ranked Neighborhoods. ICDM Workshops 2015: 831-838 - [c22]Sergey Paramonov, Matthijs van Leeuwen, Marc Denecker
An Exercise in Declarative Modeling for Relational Query Mining. ILP 2015: 166-182 - [c21]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt
Rank Matrix Factorisation. PAKDD (1) 2015: 734-746 - [c20]Matthijs van Leeuwen, Lara Cardinaels:
VIPER - Visual Pattern Explorer. ECML/PKDD (3) 2015: 333-336 - [c19]Matthijs van Leeuwen, Antti Ukkonen:
Same bang, fewer bucks: efficient discovery of the cost-influence skyline. SDM 2015: 19-27 - [e3]Élisa Fromont, Tijl De Bie, Matthijs van Leeuwen:
Advances in Intelligent Data Analysis XIV - 14th International Symposium, IDA 2015, Saint Etienne, France, October 22-24, 2015, Proceedings. Lecture Notes in Computer Science 9385, Springer 2015, ISBN 978-3-319-24464-8 [contents] - 2014
- [j6]Vladimir Dzyuba
Interactive Learning of Pattern Rankings. Int. J. Artif. Intell. Tools 23(6) (2014) - [j5]Simon Pool, Francesco Bonchi, Matthijs van Leeuwen:
Description-Driven Community Detection. ACM Trans. Intell. Syst. Technol. 5(2): 28:1-28:28 (2014) - [c18]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Ana Carolina Fierro, Kathleen Marchal, Luc De Raedt
Ranked Tiling. ECML/PKDD (2) 2014: 98-113 - [c17]Matthijs van Leeuwen, Antti Ukkonen:
Fast Estimation of the Pattern Frequency Spectrum. ECML/PKDD (2) 2014: 114-129 - [p2]Matthijs van Leeuwen, Jilles Vreeken
Mining and Using Sets of Patterns through Compression. Frequent Pattern Mining 2014: 165-198 - [p1]Matthijs van Leeuwen:
Interactive Data Exploration Using Pattern Mining. Interactive Knowledge Discovery and Data Mining in Biomedical Informatics 2014: 169-182 - [e2]Hendrik Blockeel
Advances in Intelligent Data Analysis XIII - 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 - November 1, 2014. Proceedings. Lecture Notes in Computer Science 8819, Springer 2014, ISBN 978-3-319-12570-1 [contents] - [i1]Matthijs van Leeuwen, Antti Ukkonen:
Estimating the pattern frequency spectrum inside the browser. CoRR abs/1409.7311 (2014) - 2013
- [c16]Vladimir Dzyuba
Active Preference Learning for Ranking Patterns. ICTAI 2013: 532-539 - [c15]Vladimir Dzyuba
Interactive Discovery of Interesting Subgroup Sets. IDA 2013: 150-161 - [c14]Matthijs van Leeuwen, Antti Ukkonen:
Discovering Skylines of Subgroup Sets. ECML/PKDD (3) 2013: 272-287 - [e1]Duen Horng Chau, Jilles Vreeken, Matthijs van Leeuwen, Christos Faloutsos:
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, IDEA@KDD 2013, Chicago, Illinois, USA, August 11, 2013. ACM 2013, ISBN 978-1-4503-2329-1 [contents] - 2012
- [j4]Matthijs van Leeuwen, Arno J. Knobbe
Diverse subgroup set discovery. Data Min. Knowl. Discov. 25(2): 208-242 (2012) - [c13]Thanh Le Van, Ana Carolina Fierro, Tias Guns
Mining Local Staircase Patterns in Noisy Data. ICDM Workshops 2012: 139-146 - [c12]Matthijs van Leeuwen, Diyah Puspitaningrum
Improving Tag Recommendation Using Few Associations. IDA 2012: 184-194 - 2011
- [j3]Jilles Vreeken
Krimp: mining itemsets that compress. Data Min. Knowl. Discov. 23(1): 169-214 (2011) - [c11]Matthijs van Leeuwen, Arno J. Knobbe
Non-redundant Subgroup Discovery in Large and Complex Data. ECML/PKDD (3) 2011: 459-474 - [c10]Francesco Bonchi, Matthijs van Leeuwen, Antti Ukkonen:
Characterizing Uncertain Data using Compression. SDM 2011: 534-545 - 2010
- [b1]Matthijs van Leeuwen:
Patterns that matter. Utrecht University, Netherlands, 2010 - [j2]Matthijs van Leeuwen:
Maximal exceptions with minimal descriptions. Data Min. Knowl. Discov. 21(2): 259-276 (2010) - [c9]Wouter Duivesteijn, Arno J. Knobbe
Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. ICDM 2010: 158-167
2000 – 2009
- 2009
- [j1]Matthijs van Leeuwen, Jilles Vreeken
Identifying the components. Data Min. Knowl. Discov. 19(2): 176-193 (2009) - [c8]Matthijs van Leeuwen, Francesco Bonchi, Börkur Sigurbjörnsson, Arno Siebes:
Compressing tags to find interesting media groups. CIKM 2009: 1147-1156 - [c7]Matthijs van Leeuwen, Jilles Vreeken, Arno Siebes:
Identifying the Components. ECML/PKDD (1) 2009: 32 - 2008
- [c6]Matthijs van Leeuwen, Arno Siebes:
StreamKrimp: Detecting Change in Data Streams. ECML/PKDD (1) 2008: 672-687 - 2007
- [c5]Jilles Vreeken
Preserving Privacy through Data Generation. ICDM 2007: 685-690 - [c4]Jilles Vreeken
Characterising the difference. KDD 2007: 765-774 - 2006
- [c3]Matthijs van Leeuwen, Jilles Vreeken
Compression Picks Item Sets That Matter. PKDD 2006: 585-592 - [c2]Arno Siebes, Jilles Vreeken
Item Sets that Compress. SDM 2006: 395-406 - 2002
- [c1]Jean-Christophe Zufferey, Dario Floreano, Matthijs van Leeuwen, Tancredi Merenda:
Evolving Vision-Based Flying Robots. Biologically Motivated Computer Vision 2002: 592-600
Coauthor Index
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last updated on 2024-12-23 19:32 CET by the dblp team
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