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ORCIDBart Goethals
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- affiliation: University of Antwerp
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2020 – today
- 2023
[c88]Robin Verachtert
, Jeroen Craps, Lien Michiels, Bart Goethals:
The Impact of a Popularity Punishing Hyperparameter on ItemKNN Recommendation Performance. ECIR (2) 2023: 646-654- [c87]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 - [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]