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ORCIDBart Goethals
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- affiliation: University of Antwerp, Belgium
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2020 – today
- 2024
[j34]Lien Michiels
, Robin Verachtert, Andres Ferraro, Kim Falk, Bart Goethals:
A Framework and Toolkit for Testing the Correctness of Recommendation Algorithms. Trans. Recomm. Syst. 2(1): 4:1-4:45 (2024)- [c90]Joey De Pauw, Bart Goethals:
The Role of Unknown Interactions in Implicit Matrix Factorization - A Probabilistic View. RecSys 2024: 219-227 - 2023
- [j33]Olivier Jeunen, Bart Goethals:
Pessimistic Decision-Making for Recommender Systems. Trans. Recomm. Syst. 1(1): 1-27 (2023) - [c89]Robin Verachtert, Jeroen Craps, Lien Michiels, Bart Goethals:
The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance. ECIR (2) 2023: 646-654 - [c88]Lien Michiels, Jorre T. A. Vannieuwenhuyze, Jens Leysen, Robin Verachtert, Annelien Smets, Bart Goethals:
How Should We Measure Filter Bubbles? A Regression Model and Evidence for Online News. RecSys 2023: 640-651 - [c87]Mozhgan Karimi, Boris Cule, Bart Goethals:
Leveraging Sequential Episode Mining for Session-Based News Recommendation. WISE 2023: 594-608 - [e10]Toon Calders, Celine Vens, Jefrey Lijffijt, Bart Goethals:
Artificial Intelligence and Machine Learning - 34th Joint Benelux Conference, BNAIC/Benelearn 2022, Mechelen, Belgium, November 7-9, 2022, Revised Selected Papers. Communications in Computer and Information Science 1805, Springer 2023, ISBN 978-3-031-39143-9 [contents] - [e9]Bart Goethals, Céline Robardet, Arno Siebes:
Proceedings of the 20th anniversary Workshop on Knowledge Discovery in Inductive Databases co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2022 (ECMLPKDD 2022), Grenoble, France, September 19-23, 2022. CEUR Workshop Proceedings 3334, CEUR-WS.org 2023 [contents] - [i9]Len Feremans, Boris Cule, Bart Goethals:
Efficient pattern-based anomaly detection in a network of multivariate devices. CoRR abs/2305.05538 (2023) - 2022
- [j32]Len Feremans, Boris Cule, Bart Goethals:
PETSC: pattern-based embedding for time series classification. Data Min. Knowl. Discov. 36(3): 1015-1061 (2022) - [j31]Sandy Moens, Boris Cule, Bart Goethals:
RASCL: a randomised approach to subspace clusters. Int. J. Data Sci. Anal. 14(3): 243-259 (2022) - [j30]Olivier Jeunen, Jan Van Balen, Bart Goethals:
Embarrassingly shallow auto-encoders for dynamic collaborative filtering. User Model. User Adapt. Interact. 32(4): 509-541 (2022) - [c86]Len Feremans, Robin Verachtert, Bart Goethals:
A Neighbourhood-based Location- and Time-aware Recommender System. ORSUM@RecSys 2022 - [c85]Lien Michiels, Robin Verachtert, Kim Falk, Bart Goethals:
Abstract: Should Algorithm Evaluation Extend to Testing? We Think So. Perspectives@RecSys 2022 - [c84]Joey De Pauw, Koen Ruymbeek, Bart Goethals:
Who do you think I am? Interactive User Modelling with Item Metadata. RecSys 2022: 640-643 - [c83]Lien Michiels, Robin Verachtert, Bart Goethals:
RecPack: An(other) Experimentation Toolkit for Top-N Recommendation using Implicit Feedback Data. RecSys 2022: 648-651 - [c82]Robin Verachtert, Lien Michiels, Bart Goethals:
Are We Forgetting Something? Correctly Evaluate a Recommender System With an Optimal Training Window. Perspectives@RecSys 2022 - [c81]Lien Michiels, Jens Leysen, Annelien Smets, Bart Goethals:
What Are Filter Bubbles Really? A Review of the Conceptual and Empirical Work. UMAP (Adjunct Publication) 2022: 274-279 - [i8]Joey De Pauw, Koen Ruymbeek, Bart Goethals:
Modelling Users with Item Metadata for Explainable and Interactive Recommendation. CoRR abs/2207.00350 (2022) - 2021
- [c80]Olivier Jeunen, Bart Goethals:
Pessimistic Reward Models for Off-Policy Learning in Recommendation. RecSys 2021: 63-74 - [c79]Olivier Jeunen, Bart Goethals:
Top-K Contextual Bandits with Equity of Exposure. RecSys 2021: 310-320 - [c78]Jan Van Balen, Bart Goethals:
High-dimensional Sparse Embeddings for Collaborative Filtering. WWW 2021: 575-581 - 2020
- [j29]Len Feremans, Boris Cule, Celine Vens, Bart Goethals:
Combining instance and feature neighbours for extreme multi-label classification. Int. J. Data Sci. Anal. 10(3): 215-231 (2020) - [c77]Olivier Jeunen, Jan Van Balen, Bart Goethals:
Closed-Form Models for Collaborative Filtering with Side-Information. RecSys 2020: 651-656
2010 – 2019
- 2019
- [j28]Benjamin Lucas, Ahmed Shifaz, Charlotte Pelletier, Lachlan O'Neill, Nayyar Abbas Zaidi, Bart Goethals, François Petitjean, Geoffrey I. Webb:
Proximity Forest: an effective and scalable distance-based classifier for time series. Data Min. Knowl. Discov. 33(3): 607-635 (2019) - [j27]Boris Cule, Len Feremans, Bart Goethals:
Efficiently mining cohesion-based patterns and rules in event sequences. Data Min. Knowl. Discov. 33(4): 1125-1182 (2019) - [j26]Pieter Meysman, Yvan Saeys, Ehsan Sabaghian, Wout Bittremieux, Yves Van de Peer, Bart Goethals, Kris Laukens:
Mining the Enriched Subgraphs for Specific Vertices in a Biological Graph. IEEE ACM Trans. Comput. Biol. Bioinform. 16(5): 1496-1507 (2019) - [c76]Joey De Pauw, Sandy Moens, Bart Goethals:
SubSect - An Interactive Itemset Visualization. BNAIC/BENELEARN 2019 - [c75]Joey De Pauw, Sandy Moens, Bart Goethals:
SubSect - An Interactive Itemset Visualization. BNAIC/BENELEARN (Selected Papers) 2019: 165-181 - [c74]Sandy Moens, Boris Cule, Bart Goethals:
A Sampling-Based Approach for Discovering Subspace Clusters. DS 2019: 61-71 - [c73]Len Feremans, Vincent Vercruyssen, Wannes Meert, Boris Cule, Bart Goethals:
A Framework for Pattern Mining and Anomaly Detection in Multi-dimensional Time Series and Event Logs. NFMCP@PKDD/ECML 2019: 3-20 - [c72]Len Feremans, Vincent Vercruyssen, Boris Cule, Wannes Meert, Bart Goethals:
Pattern-Based Anomaly Detection in Mixed-Type Time Series. ECML/PKDD (1) 2019: 240-256 - [c71]Mozhgan Karimi, Boris Cule, Bart Goethals:
On-the-Fly News Recommendation Using Sequential Patterns. INRA@RecSys 2019: 29-34 - [c70]Olivier Jeunen, Koen Verstrepen, Bart Goethals:
Efficient similarity computation for collaborative filtering in dynamic environments. RecSys 2019: 251-259 - [c69]Sandy Moens, Olivier Jeunen, Bart Goethals:
Interactive evaluation of recommender systems with SNIPER: an episode mining approach. RecSys 2019: 538-539 - [c68]Bart Goethals:
Lessons Learned from the FIMI Workshops. EDML@SDM 2019: 42 - 2018
- [j25]Aida Mrzic, Pieter Meysman, Wout Bittremieux, Pieter Moris, Boris Cule, Bart Goethals, Kris Laukens:
Grasping frequent subgraph mining for bioinformatics applications. BioData Min. 11(1): 20:1-20:24 (2018) - [j24]Geoffrey I. Webb, Loong Kuan Lee, Bart Goethals, François Petitjean:
Analyzing concept drift and shift from sample data. Data Min. Knowl. Discov. 32(5): 1179-1199 (2018) - [c67]Len Feremans, Boris Cule, Bart Goethals:
Mining Top-k Quantile-based Cohesive Sequential Patterns. SDM 2018: 90-98 - [r2]Bart Goethals:
Apriori Property and Breadth-First Search Algorithms. Encyclopedia of Database Systems (2nd ed.) 2018 - [i7]Benjamin Lucas, Ahmed Shifaz, Charlotte Pelletier, Lachlan O'Neill, Nayyar Abbas Zaidi, Bart Goethals, François Petitjean, Geoffrey I. Webb:
Proximity Forest: An effective and scalable distance-based classifier for time series. CoRR abs/1808.10594 (2018) - 2017
- [j23]Elyne Scheurwegs, Kim Luyckx, Léon Luyten, Bart Goethals, Walter Daelemans:
Assigning clinical codes with data-driven concept representation on Dutch clinical free text. J. Biomed. Informatics 69: 118-127 (2017) - [j22]Koen Verstrepen, Kanishka Bhaduri, Boris Cule, Bart Goethals:
Collaborative Filtering for Binary, Positiveonly Data. SIGKDD Explor. 19(1): 1-21 (2017) - [c66]Len Feremans, Boris Cule, Celine Vens, Bart Goethals:
Combining Instance and Feature Neighbors for Efficient Multi-label Classification. DSAA 2017: 109-118 - [c65]Joeri Rammelaere, Floris Geerts, Bart Goethals:
Cleaning Data with Forbidden Itemsets. ICDE 2017: 897-908 - [i6]Geoffrey I. Webb, Loong Kuan Lee, François Petitjean, Bart Goethals:
Understanding Concept Drift. CoRR abs/1704.00362 (2017) - 2016
- [j21]Cheng Zhou, Boris Cule, Bart Goethals:
Pattern Based Sequence Classification. IEEE Trans. Knowl. Data Eng. 28(5): 1285-1298 (2016) - [c64]Élisa Fromont, Bart Goethals:
k-Morik: Mining Patterns to Classify Cartified Images of Katharina. Solving Large Scale Learning Tasks 2016: 377-385 - [c63]Thomas Van Brussel, Emmanuel Müller, Bart Goethals:
Discovering Overlapping Quantitative Associations by Density-Based Mining of Relevant Attributes. FoIKS 2016: 131-148 - [c62]Boris Cule, Len Feremans, Bart Goethals:
Efficient Discovery of Sets of Co-occurring Items in Event Sequences. ECML/PKDD (1) 2016: 361-377 - 2015
- [j20]Stefan Naulaerts, Pieter Meysman, Wout Bittremieux, Trung-Nghia Vu, Wim Vanden Berghe, Bart Goethals, Kris Laukens:
A primer to frequent itemset mining for bioinformatics. Briefings Bioinform. 16(2): 216-231 (2015) - [j19]Pieter Meysman, Cheng Zhou, Boris Cule, Bart Goethals, Kris Laukens:
Mining the entire Protein DataBank for frequent spatially cohesive amino acid patterns. BioData Min. 8: 4 (2015) - [j18]Cheng Zhou, Boris Cule, Bart Goethals:
A pattern based predictor for event streams. Expert Syst. Appl. 42(23): 9294-9306 (2015) - [c61]Emin Aksehirli, Bart Goethals, Emmanuel Müller:
Efficient Cluster Detection by Ordered Neighborhoods. DaWaK 2015: 15-27 - [c60]Cheng Zhou, Boris Cule, Bart Goethals:
Cohesion based co-location pattern mining. DSAA 2015: 1-10 - [c59]Emin Aksehirli, Siegfried Nijssen, Matthijs van Leeuwen, Bart Goethals:
Finding Subspace Clusters Using Ranked Neighborhoods. ICDM Workshops 2015: 831-838 - [c58]Christophe Van Gysel, Bart Goethals, Maarten de Rijke:
Determining the Presence of Political Parties in Social Circles. ICWSM 2015: 690-693 - [c57]Tayena Hendrickx, Boris Cule, Pieter Meysman, Stefan Naulaerts, Kris Laukens, Bart Goethals:
Mining Association Rules in Graphs Based on Frequent Cohesive Itemsets. PAKDD (2) 2015: 637-648 - [c56]Koen Verstrepen, Bart Goethals:
Top-N Recommendation for Shared Accounts. RecSys 2015: 59-66 - 2014
- [j17]Toon Calders, Nele Dexters, Joris J. M. Gillis, Bart Goethals:
Mining frequent itemsets in a stream. Inf. Syst. 39: 233-255 (2014) - [j16]Boris Cule, Nikolaj Tatti, Bart Goethals:
MARBLES: Mining association rules buried in long event sequences. Stat. Anal. Data Min. 7(2): 93-110 (2014) - [j15]Cheng Zhou, Pieter Meysman, Boris Cule, Kris Laukens, Bart Goethals:
Discovery of Spatially Cohesive Itemsets in Three-Dimensional Protein Structures. IEEE ACM Trans. Comput. Biol. Bioinform. 11(5): 814-825 (2014) - [c55]Tayena Hendrickx, Boris Cule, Bart Goethals:
Mining Cohesive Itemsets in Graphs. Discovery Science 2014: 111-122 - [c54]Sandy Moens, Mario Boley, Bart Goethals:
Providing Concise Database Covers Instantly by Recursive Tile Sampling. Discovery Science 2014: 216-227 - [c53]Philip S. Yu, Masaru Kitsuregawa, Hiroshi Motoda, Bart Goethals, Minyi Guo, Longbing Cao, George Karypis, Irwin King, Wei Wang:
Welcome from DSAA 2014 chairs. DSAA 2014: 9-10 - [c52]Koen Verstrepen, Bart Goethals:
Unifying nearest neighbors collaborative filtering. RecSys 2014: 177-184 - 2013
- [c51]Sandy Moens, Emin Aksehirli, Bart Goethals:
Frequent Itemset Mining for Big Data. IEEE BigData 2013: 111-118 - [c50]Emin Aksehirli, Bart Goethals, Emmanuel Müller, Jilles Vreeken:
Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data. ICDM 2013: 937-942 - [c49]Cheng Zhou, Pieter Meysman, Boris Cule, Kris Laukens, Bart Goethals:
Mining spatially cohesive itemsets in protein molecular structures. BIOKDD 2013: 42-50 - [c48]Sandy Moens, Bart Goethals:
Randomly sampling maximal itemsets. IDEA@KDD 2013: 79-86 - [c47]Antonio Gomariz, Manuel Campos, Roque Marín, Bart Goethals:
ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences. PAKDD (1) 2013: 50-61 - [c46]Boris Cule, Bart Goethals, Tayena Hendrickx:
Mining Interesting Itemsets in Graph Datasets. PAKDD (1) 2013: 237-248 - [c45]Cheng Zhou, Boris Cule, Bart Goethals:
Itemset Based Sequence Classification. ECML/PKDD (1) 2013: 353-368 - 2012
- [j14]Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet:
An inductive database system based on virtual mining views. Data Min. Knowl. Discov. 24(1): 247-287 (2012) - [j13]Gabor Melli, Xindong Wu, Paul Beinat, Francesco Bonchi, Longbing Cao, Rong Duan, Christos Faloutsos, Rayid Ghani, Brendan Kitts, Bart Goethals, Geoffrey J. McLachlan, Jian Pei, Ashok Srivastava, Osmar R. Zaïane:
Top-10 Data Mining Case Studies. Int. J. Inf. Technol. Decis. Mak. 11(2): 389-400 (2012) - [j12]Bart Goethals, Dominique Laurent, Wim Le Page, Cheikh Tidiane Dieng:
Mining frequent conjunctive queries in relational databases through dependency discovery. Knowl. Inf. Syst. 33(3): 655-684 (2012) - [c44]Bart Goethals:
Cartification: From Similarities to Itemset Frequencies. ICFCA 2012: 4 - [c43]Boris Cule, Nikolaj Tatti, Bart Goethals:
MARBLES: Mining Association Rules Buried in Long Event Sequences. SDM 2012: 248-259 - [e8]Mohammed Javeed Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, Xindong Wu:
12th IEEE International Conference on Data Mining, ICDM 2012, Brussels, Belgium, December 10-13, 2012. IEEE Computer Society 2012, ISBN 978-1-4673-4649-8 [contents] - [e7]Jilles Vreeken, Charles Ling, Mohammed Javeed Zaki, Arno Siebes, Jeffrey Xu Yu, Bart Goethals, Geoffrey I. Webb, Xindong Wu:
12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, Brussels, Belgium, December 10, 2012. IEEE Computer Society 2012, ISBN 978-1-4673-5164-5 [contents] - 2011
- [j11]Trung-Nghia Vu, Dirk Valkenborg, Koen Smets, Kim A. Verwaest, Roger Dommisse, Filip Lemière, Alain Verschoren, Bart Goethals, Kris Laukens:
An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data. BMC Bioinform. 12: 405 (2011) - [c42]Jeroen De Knijf, Anthony M. L. Liekens, Bart Goethals:
"Tell Me More": Finding Related Items from User Provided Feedback. Discovery Science 2011: 76-90 - [c41]Jeroen De Knijf, Anthony M. L. Liekens, Walter Daelemans, Peter De Rijk, Jurgen Del-Favero, Bart Goethals:
BioGraph: Knowledge Discovery and Exploration in the Biomedical Domain. ICDM Workshops 2011: 1223-1226 - [c40]Boris Cule, Bart Goethals, Sven Tassenoy, Sabine Verboven:
Mining Train Delays. IDA 2011: 113-124 - [c39]Jeroen De Knijf, Anthony M. L. Liekens, Bart Goethals:
GaMuSo: Graph Base Music Recommendation in a Social Bookmarking Service. IDA 2011: 138-149 - [c38]Bart Goethals, Sandy Moens, Jilles Vreeken:
MIME: a framework for interactive visual pattern mining. KDD 2011: 757-760 - [c37]Bart Goethals:
Cartification: Turning Similarities into Itemset Frequencies. MultiClust@ECML/PKDD 2011: 4-6 - [c36]Bart Goethals, Sandy Moens, Jilles Vreeken:
MIME: A Framework for Interactive Visual Pattern Mining. ECML/PKDD (3) 2011: 634-637 - 2010
- [j10]Bart Goethals, Jian Pei:
Special issue on the best papers of SDM'10. Stat. Anal. Data Min. 3(6): 359-360 (2010) - [j9]Jilles Vreeken, Nikolaj Tatti, Bart Goethals:
Useful patterns (UP'10) ACM SIGKDD workshop report. SIGKDD Explor. 12(2): 56-58 (2010) - [c35]Bart Goethals, Dominique Laurent, Wim Le Page:
Discovery and Application of Functional Dependencies in Conjunctive Query Mining. DaWak 2010: 142-156 - [c34]Toon Calders, Calin Garboni, Bart Goethals:
Approximation of Frequentness Probability of Itemsets in Uncertain Data. ICDM 2010: 749-754 - [c33]Ahmed Lamkanfi, Serge Demeyer, Emanuel Giger, Bart Goethals:
Predicting the severity of a reported bug. MSR 2010: 1-10 - [c32]Boris Cule, Bart Goethals:
Mining Association Rules in Long Sequences. PAKDD (1) 2010: 300-309 - [c31]Toon Calders, Calin Garboni, Bart Goethals:
Efficient Pattern Mining of Uncertain Data with Sampling. PAKDD (1) 2010: 480-487 - [c30]Bart Goethals, Wim Le Page, Michael Mampaey:
Mining interesting sets and rules in relational databases. SAC 2010: 997-1001 - [p4]Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet:
A Practical Comparative Study Of Data Mining Query Languages. Inductive Databases and Constraint-Based Data Mining 2010: 59-77 - [p3]Hendrik Blockeel, Toon Calders, Élisa Fromont, Adriana Prado, Bart Goethals, Céline Robardet:
Inductive Querying with Virtual Mining Views. Inductive Databases and Constraint-Based Data Mining 2010: 265-287 - [p2]Bart Goethals:
Frequent Set Mining. Data Mining and Knowledge Discovery Handbook 2010: 321-338 - [e6]Saso Dzeroski, Bart Goethals, Pance Panov:
Inductive Databases and Constraint-Based Data Mining. Springer 2010, ISBN 978-1-4419-7737-3 [contents]
2000 – 2009
- 2009
- [c29]Boris Cule, Bart Goethals, Céline Robardet:
A New Constraint for Mining Sets in Sequences. SDM 2009: 317-328 - [c28]Roberto Trasarti, Francesco Bonchi, Bart Goethals:
A new technique for sequential pattern mining under regular expressions. SEBD 2009: 325-332 - [r1]Bart Goethals:
Apriori Property and Breadth-First Search Algorithms. Encyclopedia of Database Systems 2009: 124-127 - 2008
- [j8]Walter Daelemans, Bart Goethals, Katharina Morik:
Guest Editors' Introduction: Special issue of Selected Papers from ECML PKDD 2008. Data Min. Knowl. Discov. 17(1): 1-2 (2008) - [j7]Toon Calders, Nele Dexters, Bart Goethals:
Mining frequent items in a stream using flexible windows. Intell. Data Anal. 12(3): 293-304 (2008) - [j6]Walter Daelemans, Bart Goethals, Katharina Morik:
Guest Editors' introduction: special issue of selected papers from ECML PKDD 2008. Mach. Learn. 72(3): 155-156 (2008) - [c27]Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado:
Mining Views: Database Views for Data Mining. ICDE 2008: 1608-1611 - [c26]Roberto Trasarti, Francesco Bonchi, Bart Goethals:
Sequence Mining Automata: A New Technique for Mining Frequent Sequences under Regular Expressions. ICDM 2008: 1061-1066 - [c25]Hendrik Blockeel, Toon Calders, Élisa Fromont, Bart Goethals, Adriana Prado, Céline Robardet:
An inductive database prototype based on virtual mining views. KDD 2008: 1061-1064 - [c24]Bart Goethals, Wim Le Page, Heikki Mannila:
Mining Association Rules of Simple Conjunctive Queries. SDM 2008: 96-107 - [e5]Walter Daelemans, Bart Goethals, Katharina Morik:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I. Lecture Notes in Computer Science 5211, Springer 2008, ISBN 978-3-540-87478-2 [contents] - [e4]Walter Daelemans, Bart Goethals, Katharina Morik:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML/PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part II. Lecture Notes in Computer Science 5212, Springer 2008, ISBN 978-3-540-87480-5 [contents] - 2007
- [j5]Toon Calders, Bart Goethals:
Non-derivable itemset mining. Data Min. Knowl. Discov. 14(1): 171-206 (2007) - [j4]Toon Calders, Nele Dexters, Bart Goethals:
A new support measure for items in streams. Monde des Util. Anal. Données 36: 37-41 (2007) - [c23]Bart Goethals:
Finding interesting queries in relational databases. EGC 2007: 5 - [c22]Toon Calders, Nele Dexters, Bart Goethals:
Mining Frequent Itemsets in a Stream. ICDM 2007: 83-92 - [c21]Toon Calders, Bart Goethals, Michael Mampaey:
Mining itemsets in the presence of missing values. SAC 2007: 404-408 - 2006
- [c20]Toon Calders, Bart Goethals, Szymon Jaroszewicz:
Mining rank-correlated sets of numerical attributes. KDD 2006: 96-105 - [c19]Toon Calders, Bart Goethals, Adriana Prado:
Integrating Pattern Mining in Relational Databases. PKDD 2006: 454-461 - 2005
- [j3]Bart Goethals, Siegfried Nijssen, Mohammed Javeed Zaki:
Open source data mining: workshop report. SIGKDD Explor. 7(2): 143-144 (2005) - [j2]Floris Geerts, Bart Goethals, Jan Van den Bussche:
Tight upper bounds on the number of candidate patterns. ACM Trans. Database Syst. 30(2): 333-363 (2005) - [c18]Bart Goethals, Eveline Hoekx, Jan Van den Bussche:
Mining Tree Queries in a Graph. BNAIC 2005: 345-346 - [c17]Bart Goethals, Eveline Hoekx, Jan Van den Bussche:
Mining tree queries in a graph. KDD 2005: 61-69 - [c16]Toon Calders, Bart Goethals:
Quick Inclusion-Exclusion. KDID 2005: 86-103 - [c15]Bart Goethals, Juho Muhonen, Hannu Toivonen:
Mining Non-Derivable Association Rules. SDM 2005: 239-249 - [c14]Toon Calders, Bart Goethals:
Depth-First Non-Derivable Itemset Mining. SDM 2005: 250-261 - [p1]Bart Goethals:
Frequent Set Mining. The Data Mining and Knowledge Discovery Handbook 2005: 377-397 - [e3]Roberto J. Bayardo Jr., Bart Goethals, Mohammed Javeed Zaki:
FIMI '04, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Brighton, UK, November 1, 2004. CEUR Workshop Proceedings 126, CEUR-WS.org 2005 [contents] - [e2]Bart Goethals, Arno Siebes:
KDID 2004, Knowledge Discovery in Inductive Databases, Proceedings of the Third International Workshop on Knowledge Discovery inInductive Databases, Pisa, Italy, September 20, 2004, Revised Selected and Invited Papers. Lecture Notes in Computer Science 3377, Springer 2005, ISBN 3-540-25082-4 [contents] - 2004
- [j1]Bart Goethals, Mohammed Javeed Zaki:
Advances in frequent itemset mining implementations: report on FIMI'03. SIGKDD Explor. 6(1): 109-117 (2004) - [c13]Floris Geerts, Bart Goethals, Taneli Mielikäinen:
Tiling Databases. Discovery Science 2004: 278-289 - [c12]Bart Goethals, Sven Laur, Helger Lipmaa, Taneli Mielikäinen:
On Private Scalar Product Computation for Privacy-Preserving Data Mining. ICISC 2004: 104-120 - [c11]Francesco Bonchi, Bart Goethals:
FP-Bonsai: The Art of Growing and Pruning Small FP-Trees. PAKDD 2004: 155-160 - [c10]Bart Goethals:
Memory issues in frequent itemset mining. SAC 2004: 530-534 - 2003
- [c9]Bart Goethals, Mohammed Javeed Zaki:
Advances in Frequent Itemset Mining Implementations: Introduction to FIMI03. FIMI 2003 - [c8]Floris Geerts, Bart Goethals, Taneli Mielikäinen:
What You Store is What You Get. KDID 2003: 60-69 - [c7]Toon Calders, Bart Goethals:
Minimal k-Free Representations of Frequent Sets. PKDD 2003: 71-82 - [e1]Bart Goethals, Mohammed Javeed Zaki:
FIMI '03, Frequent Itemset Mining Implementations, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, 19 December 2003, Melbourne, Florida, USA. CEUR Workshop Proceedings 90, CEUR-WS.org 2003 [contents] - 2002
- [b1]Bart Goethals:
Efficient frequent pattern mining. Hasselt University, Belgium, 2002 - [c6]Bart Goethals, Jan Van den Bussche:
Relational Association Rules: Getting WARMeR. Pattern Detection and Discovery 2002: 125-139 - [c5]Toon Calders, Bart Goethals:
Mining All Non-derivable Frequent Itemsets. PKDD 2002: 74-85 - [i5]Toon Calders, Bart Goethals:
Mining All Non-Derivable Frequent Itemsets. CoRR cs.DB/0206004 (2002) - [i4]Bart Goethals, Jan Van den Bussche:
Relational Association Rules: getting WARMeR. CoRR cs.DB/0206023 (2002) - 2001
- [c4]Floris Geerts, Bart Goethals, Jan Van den Bussche:
A Tight Upper Bound on the Number of Candidate Patterns. ICDM 2001: 155-162 - [i3]Floris Geerts, Bart Goethals, Jan Van den Bussche:
A Tight Upper Bound on the Number of Candidate Patterns. CoRR cs.DB/0112007 (2001) - [i2]Bart Goethals, Jan Van den Bussche:
Interactive Constrained Association Rule Mining. CoRR cs.DB/0112011 (2001) - [i1]Tom Brijs, Bart Goethals, Gilbert Swinnen, Koen Vanhoof, Geert Wets:
A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model. CoRR cs.DB/0112013 (2001) - 2000
- [c3]Bart Goethals, Jan Van den Bussche:
On Supporting Interactive Association Rule Mining. DaWaK 2000: 307-316 - [c2]Tom Brijs, Bart Goethals, Gilbert Swinnen, Koen Vanhoof, Geert Wets:
A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model. KDD 2000: 300-304
1990 – 1999
- 1999
- [c1]Bart Goethals, Jan Van den Bussche:
A priori versus a posteriori filtering of association rules. 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery 1999
Coauthor Index
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to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2025-01-21 00:24 CET by the dblp team
all metadata released as open data under CC0 1.0 license
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since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
