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2020 – today
- 2024
[j24]John O. R. Aoga
, Juhee Bae, Stefanija Veljanoska, Siegfried Nijssen, Pierre Schaus:
Impact of Weather Factors on Migration Intention Using Machine Learning Algorithms. Oper. Res. Forum 5(1): 8 (2024)- [c82]Alexandre Dubray, Pierre Schaus, Siegfried Nijssen:
Anytime Weighted Model Counting with Approximation Guarantees for Probabilistic Inference. CP 2024: 10:1-10:16 - [c81]Bastián Véjar, Gaël Aglin, Ali Irfan Mahmutogullari, Siegfried Nijssen, Pierre Schaus, Tias Guns:
An Efficient Structured Perceptron for NP-Hard Combinatorial Optimization Problems. CPAIOR (2) 2024: 253-262 - [c80]Harold Silvère Kiossou, Pierre Schaus, Siegfried Nijssen, Gaël Aglin:
Efficient Lookahead Decision Trees. IDA (2) 2024: 133-144 - [c79]Valentin Lemaire, Gaël Aglin, Siegfried Nijssen:
Interpretable Quantile Regression by Optimal Decision Trees. IDA (2) 2024: 210-222 - [c78]Lucile Dierckx, Alexandre Dubray, Siegfried Nijssen:
Parameter Learning Using Approximate Model Counting. NeSy (2) 2024: 80-88 - 2023
- [j23]Hendrik Blockeel, Laurens Devos, Benoît Frénay, Géraldin Nanfack, Siegfried Nijssen:
Decision trees: from efficient prediction to responsible AI. Frontiers Artif. Intell. 6 (2023) - [c77]Alexandre Dubray, Pierre Schaus, Siegfried Nijssen:
Probabilistic Inference by Projected Weighted Model Counting on Horn Clauses. CP 2023: 15:1-15:17 - [c76]Nicolas Golenvaux, Xavier Gillard, Siegfried Nijssen, Pierre Schaus:
Partitioning a Map into Homogeneous Contiguous Regions: A Branch-And-Bound Approach Using Decision Diagrams (Short Paper). CP 2023: 45:1-45:10 - [c75]Lucile Dierckx, Rosana Veroneze, Siegfried Nijssen:
RL-Net: Interpretable Rule Learning with Neural Networks. NeSy 2023: 414-415 - [c74]Lucile Dierckx, Rosana Veroneze, Siegfried Nijssen:
RL-Net: Interpretable Rule Learning with Neural Networks. PAKDD (1) 2023: 95-107 - [c73]Julien Liénard, Kim Mens, Siegfried Nijssen:
Extracting Unit Tests from Patterns Mined in Student Code to Provide Improved Feedback in Autograders. SATToSE 2023: 48-56 - [e5]Bruno Crémilleux, Sibylle Hess, Siegfried Nijssen:
Advances in Intelligent Data Analysis XXI - 21st International Symposium on Intelligent Data Analysis, IDA 2023, Louvain-la-Neuve, Belgium, April 12-14, 2023, Proceedings. Lecture Notes in Computer Science 13876, Springer 2023, ISBN 978-3-031-30046-2 [contents] - 2022
- [j22]Anna L. D. Latour, Behrouz Babaki, Daniël Fokkinga, Marie Anastacio, Holger H. Hoos, Siegfried Nijssen:
Exact stochastic constraint optimisation with applications in network analysis. Artif. Intell. 304: 103650 (2022) - [c72]Alexandre Dubray, Guillaume Derval, Siegfried Nijssen, Pierre Schaus:
Optimal Decoding of Hidden Markov Models with Consistency Constraints. DS 2022: 407-417 - [c71]Lucile Dierckx, Mélanie Beauvois, Siegfried Nijssen:
Detection and Multi-label Classification of Bats. IDA 2022: 53-65 - [c70]Siegfried Nijssen, Pierre Schaus:
From Inductive Databases to DL8.5 (short paper). KDID 2022: 23-30 - [c69]Gaël Aglin, Siegfried Nijssen, Pierre Schaus:
Learning Optimal Decision Trees Under Memory Constraints. ECML/PKDD (5) 2022: 393-409 - [c68]Harold Silvère Kiossou, Pierre Schaus, Siegfried Nijssen, Vinasétan Ratheil Houndji:
Time Constrained DL8.5 Using Limited Discrepancy Search. ECML/PKDD (5) 2022: 443-459 - 2021
- [j21]Alex Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus:
Generic Constraint-based Block Modeling using Constraint Programming. J. Artif. Intell. Res. 70: 597-630 (2021) - [c67]Gaël Aglin, Siegfried Nijssen, Pierre Schaus:
Assessing Optimal Forests of Decision Trees. ICTAI 2021: 32-39 - [c66]Alexandre Dubray, Siegfried Nijssen, Isabelle Thomas, Pierre Schaus:
A Seriation Based Framework to Visualize Multiple Aspects of Road Transport from GPS Trajectories. ITSC 2021: 1379-1384 - [c65]Gauthier Van Vracem, Siegfried Nijssen:
Iterated Matrix Reordering. ECML/PKDD (3) 2021: 745-761 - [c64]Kim Mens, Siegfried Nijssen, Hoang-Son Pham:
The good, the bad, and the ugly: mining for patterns in student source code. EASEAI@ESEC/SIGSOFT FSE 2021: 1-8 - 2020
- [j20]Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, Pierre Schaus:
Learning optimal decision trees using constraint programming. Constraints An Int. J. 25(3-4): 226-250 (2020) - [j19]Marvin Meeng, Harm de Vries, Peter A. Flach, Siegfried Nijssen, Arno J. Knobbe:
Uni- and multivariate probability density models for numeric subgroup discovery. Intell. Data Anal. 24(6): 1403-1439 (2020) - [c63]Gaël Aglin, Siegfried Nijssen, Pierre Schaus:
Learning Optimal Decision Trees Using Caching Branch-and-Bound Search. AAAI 2020: 3146-3153 - [c62]Alex Lucía Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus:
Constraint Programming for an Efficient and Flexible Block Modeling Solver. AAAI 2020: 13685-13688 - [c61]Alexandre Dubray, Guillaume Derval, Siegfried Nijssen, Pierre Schaus:
Mining Constrained Regions of Interest: An Optimization Approach. DS 2020: 630-644 - [c60]Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, Pierre Schaus:
Learning Optimal Decision Trees using Constraint Programming (Extended Abstract). IJCAI 2020: 4765-4769 - [c59]Gaël Aglin, Siegfried Nijssen, Pierre Schaus:
PyDL8.5: a Library for Learning Optimal Decision Trees. IJCAI 2020: 5222-5224 - [i9]Harold Silvère Kiossou, Yannik Schenk, Frédéric Docquier, Vinasétan Ratheil Houndji, Siegfried Nijssen, Pierre Schaus:
Using an interpretable Machine Learning approach to study the drivers of International Migration. CoRR abs/2006.03560 (2020) - [i8]John O. R. Aoga, Juhee Bae, Stefanija Veljanoska, Siegfried Nijssen, Pierre Schaus:
Impact of weather factors on migration intention using machine learning algorithms. CoRR abs/2012.02794 (2020)
2010 – 2019
- 2019
- [c58]Anna Louise D. Latour, Behrouz Babaki, Siegfried Nijssen:
Stochastic Constraint Propagation for Mining Probabilistic Networks. BNAIC/BENELEARN 2019 - [c57]Alex Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus:
Generic Constraint-Based Block Modeling Using Constraint Programming. BNAIC/BENELEARN 2019 - [c56]Hélène Verhaeghe, Siegfried Nijssen, Gilles Pesant, Claude-Guy Quimper, Pierre Schaus:
Learning Optimal Decision Trees Using Constraint Programming. BNAIC/BENELEARN 2019 - [c55]John O. R. Aoga, Siegfried Nijssen, Pierre Schaus:
Modeling Pattern Set Mining Using Boolean Circuits. CP 2019: 621-638 - [c54]Alex Mattenet, Ian Davidson, Siegfried Nijssen, Pierre Schaus:
Generic Constraint-Based Block Modeling Using Constraint Programming. CP 2019: 656-673 - [c53]Hoang-Son Pham, Siegfried Nijssen, Kim Mens, Dario Di Nucci, Tim Molderez, Coen De Roover, Johan Fabry, Vadim Zaytsev:
Mining Patterns in Source Code Using Tree Mining Algorithms. DS 2019: 471-480 - [c52]Anna Louise D. Latour, Behrouz Babaki, Siegfried Nijssen:
Stochastic Constraint Propagation for Mining Probabilistic Networks. IJCAI 2019: 1137-1145 - [c51]Dario Di Nucci, Hoang-Son Pham, Johan Fabry, Coen De Roover, Kim Mens, Tim Molderez, Siegfried Nijssen, Vadim Zaytsev:
A Language-Parametric Modular Framework for Mining Idiomatic Code Patterns. SATToSE 2019 - 2018
- [c50]John O. R. Aoga, Tias Guns, Siegfried Nijssen, Pierre Schaus:
Finding Probabilistic Rule Lists using the Minimum Description Length Principle. DS 2018: 66-82 - [c49]Thomas Bollen, Guillaume Leurquin, Siegfried Nijssen:
ConvoMap: Using Convolution to Order Boolean Data. IDA 2018: 62-74 - [i7]Anna L. D. Latour, Behrouz Babaki, Siegfried Nijssen:
Stochastic Constraint Optimization using Propagation on Ordered Binary Decision Diagrams. CoRR abs/1807.01079 (2018) - 2017
- [j18]Tias Guns, Anton Dries, Siegfried Nijssen, Guido Tack, Luc De Raedt:
MiningZinc: A declarative framework for constraint-based mining. Artif. Intell. 244: 6-29 (2017) - [j17]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. IEEE Intell. Syst. 32(5): 44-52 (2017) - [j16]Thanh Le Van, Siegfried Nijssen, Matthijs van Leeuwen, Luc De Raedt:
Semiring Rank Matrix Factorization. IEEE Trans. Knowl. Data Eng. 29(8): 1737-1750 (2017) - [c48]Florian Demesmaeker, Amine Ghrab, Siegfried Nijssen, Sabri Skhiri:
Discovering interesting patterns in large graph cubes. IEEE BigData 2017: 3322-3331 - [c47]Anna L. D. Latour, Behrouz Babaki, Anton Dries, Angelika Kimmig, Guy Van den Broeck, Siegfried Nijssen:
Combining Stochastic Constraint Optimization and Probabilistic Programming - From Knowledge Compilation to Constraint Solving. CP 2017: 495-511 - [c46]Ricardo Cachucho, Siegfried Nijssen, Arno J. Knobbe:
Biclustering Multivariate Time Series. IDA 2017: 27-39 - [r4]Siegfried Nijssen:
Constraint-Based Mining. Encyclopedia of Machine Learning and Data Mining 2017: 274-279 - [r3]Siegfried Nijssen:
Tree Mining. Encyclopedia of Machine Learning and Data Mining 2017: 1284-1292 - 2016
- [j15]Thanh Le Van, Matthijs van Leeuwen, Ana Carolina Fierro, Dries De Maeyer, Jimmy Van den Eynden, Lieven P. C. Verbeke, Luc De Raedt, Kathleen Marchal, Siegfried Nijssen:
Simultaneous discovery of cancer subtypes and subtype features by molecular data integration. Bioinform. 32(17): 445-454 (2016) - [c45]Lambert Schomaker, Andreas Weber, Michiel Thijssen, Maarten Heerlien, Aske Plaat, Siegfried Nijssen, Fons J. Verbeek, Michael S. Lew, Eulalia Gasso Miracle, Katy Wolstencroft, Ernest Suyver, Bart Verheij, Marco A. Wiering, René Dekker, Joost N. Kok, Lissa Roberts, H. Jaap van den Herik:
Making Sense of Illustrated Handwritten Archives. DH 2016: 674-676 - [c44]Ricardo Cachucho, Kaihua Liu, Siegfried Nijssen, Arno J. Knobbe:
Bipeline: A Web-Based Visualization Tool for Biclustering of Multivariate Time Series. ECML/PKDD (3) 2016: 12-16 - [p9]Anton Dries, Tias Guns, Siegfried Nijssen, Behrouz Babaki, Thanh Le Van, Benjamin Négrevergne, Sergey Paramonov, Luc De Raedt:
Modeling in MiningZinc. Data Mining and Constraint Programming 2016: 257-281 - [p8]Valerio Grossi, Tias Guns, Anna Monreale, Mirco Nanni, Siegfried Nijssen:
Partition-Based Clustering Using Constraint Optimization. Data Mining and Constraint Programming 2016: 282-299 - [p7]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. Data Mining and Constraint Programming 2016: 303-309 - [e4]Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi:
Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach. Lecture Notes in Computer Science 10101, Springer 2016, ISBN 978-3-319-50136-9 [contents] - 2015
- [j14]Michael Mampaey, Siegfried Nijssen, Ad Feelders, Rob M. Konijn, Arno J. Knobbe:
Efficient algorithms for finding optimal binary features in numeric and nominal labeled data. Knowl. Inf. Syst. 42(2): 465-492 (2015) - [c43]Emin Aksehirli, Siegfried Nijssen, Matthijs van Leeuwen, Bart Goethals:
Finding Subspace Clusters Using Ranked Neighborhoods. ICDM Workshops 2015: 831-838 - [c42]Behrouz Babaki, Tias Guns, Siegfried Nijssen, Luc De Raedt:
Constraint-Based Querying for Bayesian Network Exploration. IDA 2015: 13-24 - [c41]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Rank Matrix Factorisation. PAKDD (1) 2015: 734-746 - [i6]Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis:
The Inductive Constraint Programming Loop. CoRR abs/1510.03317 (2015) - 2014
- [j13]Vladimir Dzyuba, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Interactive Learning of Pattern Rankings. Int. J. Artif. Intell. Tools 23(6) (2014) - [c40]Behrouz Babaki, Tias Guns, Siegfried Nijssen:
Constrained Clustering Using Column Generation. CPAIOR 2014: 438-454 - [c39]Ricardo Cachucho, Marvin Meeng, Ugo Vespier, Siegfried Nijssen, Arno J. Knobbe:
Mining multivariate time series with mixed sampling rates. UbiComp 2014: 413-423 - [c38]Thanh Le Van, Matthijs van Leeuwen, Siegfried Nijssen, Ana Carolina Fierro, Kathleen Marchal, Luc De Raedt:
Ranked Tiling. ECML/PKDD (2) 2014: 98-113 - [p6]Siegfried Nijssen, Albrecht Zimmermann:
Constraint-Based Pattern Mining. Frequent Pattern Mining 2014: 147-163 - [p5]Albrecht Zimmermann, Siegfried Nijssen:
Supervised Pattern Mining and Applications to Classification. Frequent Pattern Mining 2014: 425-442 - [i5]Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, Michèle Sebag:
Constraints, Optimization and Data (Dagstuhl Seminar 14411). Dagstuhl Reports 4(10): 1-31 (2014) - 2013
- [j12]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Guest editor's introduction: special issue of the ECML PKDD 2013 journal track. Data Min. Knowl. Discov. 27(3): 291-293 (2013) - [j11]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Guest editor's introduction: special issue of the ECML PKDD 2013 journal track. Mach. Learn. 93(1): 1-3 (2013) - [j10]Tias Guns, Siegfried Nijssen, Luc De Raedt:
k-Pattern Set Mining under Constraints. IEEE Trans. Knowl. Data Eng. 25(2): 402-418 (2013) - [c37]Sean Gilpin, Siegfried Nijssen, Ian N. Davidson:
Formalizing Hierarchical Clustering as Integer Linear Programming. AAAI 2013: 372-378 - [c36]Ugo Vespier, Siegfried Nijssen, Arno J. Knobbe:
Mining characteristic multi-scale motifs in sensor-based time series. CIKM 2013: 2393-2398 - [c35]Benjamin Négrevergne, Anton Dries, Tias Guns, Siegfried Nijssen:
Dominance Programming for Itemset Mining. ICDM 2013: 557-566 - [c34]Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt:
The MiningZinc Framework for Constraint-Based Itemset Mining. ICDM Workshops 2013: 1081-1084 - [c33]Vladimir Dzyuba, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt:
Active Preference Learning for Ranking Patterns. ICTAI 2013: 532-539 - [c32]Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt:
MiningZinc: A Modeling Language for Constraint-Based Mining. IJCAI 2013: 1365-1372 - [e3]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part I. Lecture Notes in Computer Science 8188, Springer 2013, ISBN 978-3-642-40987-5 [contents] - [e2]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Lecture Notes in Computer Science 8189, Springer 2013, ISBN 978-3-642-40990-5 [contents] - [e1]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III. Lecture Notes in Computer Science 8190, Springer 2013, ISBN 978-3-642-40993-6 [contents] - 2012
- [j9]Joris Renkens, Guy Van den Broeck, Siegfried Nijssen:
k-Optimal: a novel approximate inference algorithm for ProbLog. Mach. Learn. 89(3): 215-231 (2012) - [c31]Thanh Le Van, Ana Carolina Fierro, Tias Guns, Matthijs van Leeuwen, Siegfried Nijssen, Luc De Raedt, Kathleen Marchal:
Mining Local Staircase Patterns in Noisy Data. ICDM Workshops 2012: 139-146 - [c30]Michael Mampaey, Siegfried Nijssen, Ad Feelders, Arno J. Knobbe:
Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data. ICDM 2012: 499-508 - [c29]Ugo Vespier, Arno J. Knobbe, Siegfried Nijssen, Joaquin Vanschoren:
MDL-Based Analysis of Time Series at Multiple Time-Scales. ECML/PKDD (2) 2012: 371-386 - [c28]Anton Dries, Siegfried Nijssen:
Mining Patterns in Networks using Homomorphism. SDM 2012: 260-271 - [p4]Anton Dries, Siegfried Nijssen, Luc De Raedt:
BiQL: A Query Language for Analyzing Information Networks. Bisociative Knowledge Discovery 2012: 147-165 - [i4]Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, Filip Zelezný:
A Revised Publication Model for ECML PKDD. CoRR abs/1207.6324 (2012) - 2011
- [j8]Tias Guns, Siegfried Nijssen, Luc De Raedt:
Itemset mining: A constraint programming perspective. Artif. Intell. 175(12-13): 1951-1983 (2011) - [c27]Tias Guns, Siegfried Nijssen, Albrecht Zimmermann, Luc De Raedt:
Declarative Heuristic Search for Pattern Set Mining. ICDM Workshops 2011: 1104-1111 - [c26]Siegfried Nijssen, Aída Jiménez, Tias Guns:
Constraint-Based Pattern Mining in Multi-relational Databases. ICDM Workshops 2011: 1120-1127 - [c25]Joris Renkens, Guy Van den Broeck, Siegfried Nijssen:
k-Optimal: A Novel Approximate Inference Algorithm for ProbLog. ILP 2011: 33-38 - [c24]Luc De Raedt, Siegfried Nijssen:
Towards Programming Languages for Machine Learning and Data Mining (Extended Abstract). ISMIS 2011: 25-32 - [c23]Tias Guns, Siegfried Nijssen, Luc De Raedt:
Evaluating Pattern Set Mining Strategies in a Constraint Programming Framework. PAKDD (2) 2011: 382-394 - [i3]Anton Dries, Siegfried Nijssen:
Mining Patterns in Networks using Homomorphism. CoRR abs/1110.3225 (2011) - [i2]Björn Bringmann, Siegfried Nijssen, Albrecht Zimmermann:
Pattern-Based Classification: A Unifying Perspective. CoRR abs/1111.6191 (2011) - [i1]Luc De Raedt, Siegfried Nijssen, Barry O'Sullivan, Pascal Van Hentenryck:
Constraint Programming meets Machine Learning and Data Mining (Dagstuhl Seminar 11201). Dagstuhl Reports 1(5): 61-83 (2011) - 2010
- [j7]Siegfried Nijssen, Élisa Fromont:
Optimal constraint-based decision tree induction from itemset lattices. Data Min. Knowl. Discov. 21(1): 9-51 (2010) - [j6]Anton Dries, Luc De Raedt, Siegfried Nijssen:
Mining Predictive k-CNF Expressions. IEEE Trans. Knowl. Data Eng. 22(5): 743-748 (2010) - [c22]Luc De Raedt, Tias Guns, Siegfried Nijssen:
Constraint Programming for Data Mining and Machine Learning. AAAI 2010: 1671-1675 - [c21]Tias Guns, Hong Sun, Kathleen Marchal, Siegfried Nijssen:
Cis-regulatory module detection using constraint programming. BIBM 2010: 363-368 - [c20]Anton Dries, Siegfried Nijssen:
Analyzing graph databases by aggregate queries. MLG@KDD 2010: 37-45 - [c19]Siegfried Nijssen, Tias Guns:
Integrating Constraint Programming and Itemset Mining. ECML/PKDD (2) 2010: 467-482 - [p3]Jérémy Besson, Jean-François Boulicaut, Tias Guns, Siegfried Nijssen:
Generalizing Itemset Mining in a Constraint Programming Setting. Inductive Databases and Constraint-Based Data Mining 2010: 107-126 - [p2]Björn Bringmann, Siegfried Nijssen, Albrecht Zimmermann:
From Local Patterns to Classification Models. Inductive Databases and Constraint-Based Data Mining 2010: 127-154 - [p1]Ross D. King, Amanda C. Schierz, Amanda Clare, Jem J. Rowland, Andrew Sparkes, Siegfried Nijssen, Jan Ramon:
Inductive Queries for a Drug Designing Robot Scientist. Inductive Databases and Constraint-Based Data Mining 2010: 425-451 - [r2]Siegfried Nijssen:
Constraint-Based Mining. Encyclopedia of Machine Learning 2010: 221-225 - [r1]Siegfried Nijssen:
Tree Mining. Encyclopedia of Machine Learning 2010: 991-999
2000 – 2009
- 2009
- [j5]Jan Ramon, Siegfried Nijssen:
Polynomial-Delay Enumeration of Monotonic Graph Classes. J. Mach. Learn. Res. 10: 907-929 (2009) - [c18]Anton Dries, Siegfried Nijssen, Luc De Raedt:
A query language for analyzing networks. CIKM 2009: 485-494 - [c17]Siegfried Nijssen, Tias Guns, Luc De Raedt:
Correlated itemset mining in ROC space: a constraint programming approach. KDD 2009: 647-656 - [c16]Siegfried Nijssen, Luc De Raedt:
Grammar Mining. SDM 2009: 1026-1037 - [c15]Mathias Verbeke, Bettina Berendt, Siegfried Nijssen:
Data Mining, Interactive Semantic Structuring, and Collaboration: A Diversity-aware Method for Sense-making in Search. LivingWeb@ISWC 2009 - 2008
- [c14]Siegfried Nijssen:
Bayes optimal classification for decision trees. ICML 2008: 696-703 - [c13]Luc De Raedt, Tias Guns, Siegfried Nijssen:
Constraint programming for itemset mining. KDD 2008: 204-212 - [c12]Björn Bringmann, Siegfried Nijssen:
What Is Frequent in a Single Graph?. PAKDD 2008: 858-863 - 2007
- [c11]Siegfried Nijssen, Élisa Fromont:
Mining optimal decision trees from itemset lattices. KDD 2007: 530-539 - [c10]Jan Ramon, Siegfried Nijssen:
General Graph Refinement with Polynomial Delay. MLG 2007 - 2006
- [j4]Jeroen Kazius, Siegfried Nijssen, Joost N. Kok, Thomas Bäck, Adriaan P. IJzerman:
Substructure Mining Using Elaborate Chemical Representation. J. Chem. Inf. Model. 46(2): 597-605 (2006) - [c9]Siegfried Nijssen, Luc De Raedt:
IQL: A Proposal for an Inductive Query Language. KDID 2006: 189-207 - [c8]Björn Bringmann, Albrecht Zimmermann, Luc De Raedt, Siegfried Nijssen:
Don't Be Afraid of Simpler Patterns. PKDD 2006: 55-66 - 2005
- [j3]Yun Chi, Richard R. Muntz, Siegfried Nijssen, Joost N. Kok:
Frequent Subtree Mining - An Overview. Fundam. Informaticae 66(1-2): 161-198 (2005) - [j2]Bart Goethals, Siegfried Nijssen, Mohammed Javeed Zaki:
Open source data mining: workshop report. SIGKDD Explor. 7(2): 143-144 (2005) - [c7]Siegfried Nijssen, Joost N. Kok:
Multi-class Correlated Pattern Mining. KDID 2005: 165-187 - 2004
- [c6]Siegfried Nijssen, Joost N. Kok:
Ideal Refinement of Datalog Clauses Using Primary Keys. ECAI 2004: 520-524 - [c5]Siegfried Nijssen, Joost N. Kok:
A quickstart in frequent structure mining can make a difference. KDD 2004: 647-652 - [c4]Siegfried Nijssen, Joost N. Kok:
Frequent graph mining and its application to molecular databases. SMC (5) 2004: 4571-4577 - [c3]Siegfried Nijssen, Joost N. Kok:
The Gaston Tool for Frequent Subgraph Mining. GraBaTs 2004: 77-87 - 2003
- [j1]Siegfried Nijssen, Thomas Bäck:
An analysis of the behavior of simplified evolutionary algorithms on trap functions. IEEE Trans. Evol. Comput. 7(1): 11-22 (2003) - [c2]Siegfried Nijssen, Joost N. Kok:
Efficient Frequent Query Discovery in FARMER. PKDD 2003: 350-362 - 2001
- [c1]Siegfried Nijssen, Joost N. Kok:
Faster Association Rules for Multiple Relations. IJCAI 2001: 891-896
Coauthor Index
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last updated on 2024-10-07 21:14 CEST by the dblp team
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