2015 

A primer to frequent itemset mining for bioinformatics.
S. Naulaerts, P. Meysman, W. Bittremieux, T. Vu, W. Vanden Berghe, B. Goethals, and K. Laukens.
Briefings in bioinformatics SN  14675463, 16(2):216–231, 2015. 

Efficient Cluster Detection by Ordered Neighborhoods.
E. Aksehirli, B. Goethals, and E. Müller.
In Proceedings of the 17th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2015). 2015. 

Mining the entire Protein DataBank for frequent spatially cohesive amino acid patterns.
P. Meysman, C. Zhou, B. Cule, B. Goethals, and K. Laukens.
BioData mining, 8(1):4, BioMed Central Ltd 2015. 

Mining Association Rules in Graphs based on Frequent Cohesive Itemsets.
T. Hendrickx, B. Cule, P. Meysman, S. Naulaerts, K. Laukens, and B. Goethals.
In Proceedings of the the 19th PacificAsia Conference on Knowledge Discovery and Data Mining. Springer 2015. 

Determining the Presence of Political Parties in Social Circles.
C. Van Gysel, B. Goethals, and M. de Rijke.
In ICWSM 2015: International Conference on Weblogs and Social Media. AAAI 2015. 

TopN Recommendation for Shared Accounts.
K. Verstrepen, and B. Goethals.
In Proceedings of the 9th ACM Conference on Recommender Systems (RecSys'15). 2015. 
2014 

Mining Frequent Itemsets in a Stream.
T. Calders, N. Dexters, J. Gillis, and B. Goethals.
Informations Systems, 39:233  255, 2014. 

Interactive and Manual Construction of Classification Trees.
S. Pauwels, S. Moens, and B. Goethals.
In Proceedings of the 23rd BelgianDutch Conference on Machine Learning (BENELEARN 2014). 2014. 

MARBLES: Mining Association Rules Buried in Long Event Sequences.
B. Cule, N. Tatti, and B. Goethals.
Statistical Analysis and Data Mining, 7(2):93110, Wiley 2014. 

Providing Concise Database Covers using Recursive Tile Sampling.
S. Moens, M. Boley, and B. Goethals.
In Proceedings Discovery Science 2014, pages 216227. Springer 2014. 

Mining Cohesive Itemsets in Graphs.
T. Hendrickx, B. Cule, and B. Goethals.
In Proceedings of the 17th International conference on Discovery Science (DS2014). 2014. 

Unifying Nearest Neighbors Collaborative Filtering.
K. Verstrepen, and B. Goethals.
In Proceedings of the 8th ACM Conference on Recommender Systems (RecSys'14). 2014. 

Discovery of Spatially Cohesive Itemsets in Threedimensional Protein Structures.
C. Zhou, P. Meysman, B. Cule, K. Laukens, and B. Goethals.
IEEE/ACM Trans. Comput. Biology Bioinform., 2014. 
2013 

Itemset Based Sequence Classification.
C. Zhou, B. Cule, and B. Goethals.
In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Data (ECML PKDD 2013). Springer 2013. 

Frequent Itemset Mining for Big Data.
S. Moens, E. Aksehirli, and B. Goethals.
In SML: BigData 2013 Workshop on Scalable Machine Learning. IEEE 2013. 

Mining Spatially Cohesive Itemsets in Protein Molecular Structures.
C. Zhou, P. Meysman, B. Cule, K. Laukens, and B. Goethals.
In BioKDD'13: 12th International Workshop on Data Mining in Bioinformatics Proceedings. 2013. 

Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data.
E. Aksehirli, B. Goethals, E. Müller, and J. Vreeken.
In Data Mining, 2013. ICDM 2013. Thirteenth IEEE International Conference on. IEEE (to appear) 2013. 

Mining Interesting Itemsets in Graph Datasets.
B. Cule, B. Goethals, and T. Hendrickx.
In Proceedings of the 17th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD2013). 2013. 

ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences.
A. Gomariz, M. Campos, R. Marin, and B. Goethals.
In Proceedings of the 17th PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD2013). 2013. 

A primer to frequent itemset mining for bioinformatics.
S. Naulaerts, P. Meysman, W. Bittremieux, T. Vu, W. Vanden Berghe, B. Goethals, and K. Laukens.
Briefings in bioinformatics:1–16, 2013. 

Randomly Sampling Maximal Itemsets.
S. Moens, and B. Goethals.
In IDEA: KDD 2013 Workshop on Interactive Data Exploration and Analytics in Proc ACM SIGKDD. 2013. 
2012 

Mining Frequent Conjunctive Queries in Relational Databases through Dependency Discovery.
B. Goethals, D. Laurent, W. Le Page, and C. Dieng.
Knowledge and Information Systems, Springer To appear 2012. 

MARBLES: Mining Association Rules Buried in Long Event Sequences.
B. Cule, N. Tatti, and B. Goethals.
In Proceedings of the SIAM International Conference on Data Mining (SDM). SIAM 2012. 

An inductive database system based on virtual mining views.
H. Blockeel, T. Calders, \. Fromont, B. Goethals, A. Prado, and C. Robardet.
Data Mining and Knowledge Discovery, 24(1):247287, Springer 2012. 

Proceedings of the 12th IEEE International Conference on Data Mining (ICDM'12), IEEE, 2012.
A. Siebes, M. Zaki, J. Yu, B. Goethals, G. Webb, and X. Wu (Eds.).
IEEE 2012. 

Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW'12), IEEE, 2012.
J. Vreeken, C. Ling, A. Siebes, M. Zaki, J. Yu, B. Goethals, G. Webb, and X. Wu (Eds.).
IEEE 2012. 

Cartification: From Similarities to Itemset Frequencies.
B. Goethals.
In Proceedings of the 10th International Conference on Formal Concept Analysis (ICFCA), Vol. 7278 of Lecture Notes in Computer Science, pages 4. Springer 2012. 

Top10 Data Mining Case Studies.
G. Zaiane.
International Journal of Information Technology and Decision
Making, 11(2):389400, 2012. 
2011 

MIME: A Framework for Interactive Visual Pattern Mining.
B. Goethals, S. Moens, and J. Vreeken.
In Proceedings of the 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). ACM 2011. 

MIME: A Framework for Interactive Visual Pattern Mining.
B. Goethals, S. Moens, and J. Vreeken.
In Proceedings of ECML PKDD 2011, Vol. 6913 of Lecture Notes in Computer Science, pages 634637. Springer 2011. 

BioGraph: Unsupervised Biomedical Knowledge Discovery via Automated Hypothesis Generation.
A. Liekens, J. De Knijf, W. Daelemans, B. Goethals, P. De Rijk, and J. DelFavero.
Genome Biology, 12(6), 2011. 

Mining Train Delays.
B. Cule, B. Goethals, S. Tassenoy, and S. Verboven.
In Proceedings of the 4th International Seminar on Railway Operations Modelling and Analysis (RailRome 2011). 2011. 

Mining Train Delays.
B. Cule, B. Goethals, S. Tassenoy, and S. Verboven.
In Proceedings of the 10th International Symposium on Intelligent Data Analysis (IDA 2011). 2011. 

"Tell Me More”: Finding Related Items from User Provided Feedback.
J. De Knijf, A. Liekens, and B. Goethals.
In Proceedings of the Fourteenth International Conference on Discovery Science (DS 2011). 2011. 

GaMuSo: Graph based Music recommendation in a Social bookmarking service.
J. De Knijf, A. Liekens, and B. Goethals.
In Proceedings of the 10th International Symposium on Intelligent Data Analysis (IDA 2011). 2011. 

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data.
T. Vu, D. Valkenborg, K. Smets, K. Verwaest, R. Dommisse, F. Lemiere, A. Verschoren, B. Goethals, and K. Laukens.
BMC Bioinformatics, 12(1):405, 2011. 
2010 

Efficient Pattern Mining from Uncertain Data with Sampling.
T. Calders, C. Garboni, and B. Goethals.
In Advances in Knowledge Discovery and Data Mining, Vol. 6118 of Lecture Notes in Computer Science. Springer 2010. 

Mining Association Rules in Long Sequences.
B. Cule, and B. Goethals.
In Advances in Knowledge Discovery and Data Mining, Vol. 6118 of Lecture Notes in Computer Science. Springer 2010. 

Inductive querying with virtual mining views.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, A. Prado, and C. Robardet.
In Inductive Databases and ConstraintBased Data Mining, pages 265–287. Springer 2010. 

A practical comparative study of data mining query languages.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, A. Prado, and C. Robardet.
In Inductive Databases and ConstraintBased Data Mining, pages 59–77. Springer 2010. 

Approximating Frequentness Probability of Itemsets in Uncertain Data.
T. Calders, C. Garboni, and B. Goethals.
In Proceedings of the IEEE International Conference on Data Mining (ICDM). IEEE Computer Society 2010. 

Inductive Databases and ConstraintBased Data Mining.
S. Dzeroski, B. Goethals, and P. Panov (Eds.).
Springer 2010. 

Discovery and application of functional dependencies in conjunctive query mining.
B. Goethals, D. Laurent, and W. Le Page.
In Proceedings of DaWak 2010. Springer 2010. 

Frequent Set Mining.
B. Goethals.
In Data Mining and Knowledge Discovery Handbook, pages 321338. Springer 2010. 

Mining Interesting Sets and Rules in Relational Databases.
B. Goethals, W. Le Page, and M. Mampaey.
In Proceedings of the 25th ACM Symposium on Applied Computing (ACM SAC), Vol. 2, pages 996–1000. ACM 2010. 

Special issue on the best papers of SDM'10.
B. Goethals, and J. Pei.
Statistical Analysis and Data Mining, 3(6):359360, Wiley 2010. 

Predicting the severity of a reported bug.
A. Lamkanfi, S. Demeyer, E. Giger, and B. Goethals.
In Proceedings of the 7th IEEE Working Conference on Mining Software Repositories (MSR), pages 110. IEEE Computer Society 2010. 

Proceedings of the SIAM International Conference on Data Mining (SDM).
B. Goethals, and J. Pei (Eds.).
SIAM 2010. 

Useful patterns (UP’10): ACM SIGKDD Workshop Report.
J. Vreeken, N. Tatti, and B. Goethals.
SIGKDD Explorations, 12(2):56–58, ACM 2010. 

UP '10: Proceedings of the ACM SIGKDD Workshop on Useful Patterns 2010.
J. Vreeken, N. Tatti, and B. Goethals (Eds.).
ACM 2010. 
2009 

A new constraint for mining sets in sequences.
B. Cule, B. Goethals, and C. Robardet.
In Proceedings of the SIAM Int. Conf. on Data Mining (SDM). SIAM 2009. 

Apriori Property and BreadthFirst Search Algorithms.
B. Goethals.
In Encyclopedia of Database Systems, pages 124127. Springer 2009. 

Mining Interesting Sets and Rules in Relational Databases.
B. Goethals, W. Le Page, and M. Mampaey.
Technical Report of Universiteit Antwerpen (09.02).


Levelwise Cluster Mining under a Maximum SSE Constraint.
J. Knijf, B. Goethals, and A. Prado.
In From Local Patterns to Global Models (LeGo 2009). 2009. 

A new technique for sequential pattern mining under regular expressions.
R. Trasarti, F. Bonchi, and B. Goethals.
In Proceedings of the Italian Symposium on Advanced Database Systems (SEBD), pages 325332. Edizioni Seneca 2009. 
2008 

Automatic Vandalism Detection in Wikipedia: Towards a Machine Learning Approach.
K. Smets, B. Goethals, and B. Verdonk.
In Proceedings of the AAAI Workshop on Wikipedia and Artificial Intelligence: An Evolving Synergy (WikiAI), pages 43–48. AAAI Press 2008. 

Mining Association Rules of Simple Conjunctive Queries.
B. Goethals, W. Le Page, and H. Mannila.
In Proceedings of the SIAM Int. Conf. on Data Mining (SDM). SIAM 2008. 

Guest Editors' Introduction: Special issue of Selected Papers from ECML PKDD 2008.
W. Daelemans, B. Goethals, and K. Morik.
Data Mining Knowledge Discovery, 17(1):12, 2008. 

European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2008), Part I.
W. Daelemans, B. Goethals, and K. Morik (Eds.).
Vol. 5211 of Lecture Notes in Computer Science. Springer 2008. 

European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2008), Part 2.
W. Daelemans, B. Goethals, and K. Morik (Eds.).
Vol. 5212 of Lecture Notes in Computer Science. Springer 2008. 

Sequence Mining Automata: a New Technique for Mining Frequent Sequences Under Regular Expressions.
R. Trasarti, F. Bonchi, and B. Goethals.
In Proc. of IEEE ICDM International Conference on Data Mining. IEEE Computer Society 2008. 

Mining Frequent Items in a Stream using Flexible Windows.
T. Calders, N. Dexters, and B. Goethals.
Intelligent Data Analysis, 12(3), IOS Press May 2008. 

An Inductive Database Prototype Based on Virtual Mining Views.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, A. Prado, and C. Robardet.
In 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM 2008. 

Mining Views: Database Views for Data Mining.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, and A. Prado.
In Proceedings of the IEEE International Conference on Data Engineering (ICDE). IEEE Computer Society 2008. 

Guest Editors' introduction: special issue of selected papers from ECML PKDD 2008.
W. Daelemans, B. Goethals, and K. Morik.
Machine Learning, 72(3):155156, 2008. 
2007 

NonDerivable Itemset Mining.
T. Calders, and B. Goethals.
Data Mining and Knowledge Discovery, 14(1):171–206, Springer February 2007. 

A New Support Measure for Items in Streams.
T. Calders, N. Dexters, and B. Goethals.
Le Monde des Utilisateurs de L'Analyse de Données (La Revue MODULAD), 36:37–41, 2007. 

Mining Views: Database Views for Data Mining.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, and A. Prado.
In ECML/PKDD2007 International Workshop on ConstraintBased Mining and Learning (CMILE). 2007. 

Finding interesting queries in relational databases.
B. Goethals.
In Extraction et gestion des connaissances (EGC'2007), Vol. RNTIE9 of Revue des Nouvelles Technologies de l'Information, pages 5. Cépaduèséditions 2007. 

Mining frequent itemsets in a stream.
T. Calders, N. Dexters, and B. Goethals.
In Proceedings of the IEEE International Conference on Data Mining (ICDM), pages 83–92. IEEE Computer Society 2007. 

Mining itemsets in the presence of missing values.
T. Calders, B. Goethals, and M. Mampaey.
In Proceedings of the ACM Symposium on Applied Computing, pages 404–408. ACM 2007. 
2006 

Mining Frequent Items in a Stream Using Flexible Windows.
T. Calders, N. Dexters, and B. Goethals.
In ECML/PKDD2006 International Workshop on Knowledge Discovery from Data Streams (IWKDDS). 2006. 

Integrating Pattern Mining in Relational Databases.
T. Calders, B. Goethals, and A. Prado.
In Proceedings of the Int. Conf. Principles of Data Mining and Knowledge Discovery (PKDD). Springer 2006. 

Constraint Extraction from SQLqueries (manuscript).
T. Calders, B. Goethals, and A. Prado.


Proceedings of the 9th International Workshop on High Performance and Distributed Data Mining.
B. Goethals, and B. Park (Eds.).
2006. 

Mining RankCorrelated Sets of Numerical Attributes.
T. Calders, B. Goethals, and S. Jaroszewicz.
In 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM 2006. 
2005 

Quick InclusionExclusion.
T. Calders, and B. Goethals.
In Proceedings ECMLPKDD 2005 Workshop Knowledge Discovery in Inductive Databases, Vol. 3933 of LNCS. Springer 2005. 

Depthfirst nonderivable itemset mining.
T. Calders, and B. Goethals.
In Proceedings of the SIAM Int. Conf. on Data Mining (SDM). SIAM 2005. 

Frequent Set Mining.
B. Goethals.
In The Data Mining and Knowledge Discovery Handbook, pages 377397. Springer 2005. 

Mining Tree Queries in a Graph.
B. Goethals, E. Hoekx, and J. Van den Bussche.
In 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pages 61–69. 2005. 

Mining NonDerivable Association Rules.
B. Goethals, J. Muhonen, and H. Toivonen.
In Proc. of the SIAM International Conference on Data Mining. SIAM 2005. 

Proceedings of the ACM SIGKDD Open Source Data Mining Workshop.
B. Goethals, S. Nijssen, and M. Zaki (Eds.).
ACM 2005. 

Open source data mining: workshop report.
B. Goethals, S. Nijssen, and M. Zaki.
SIGKDD Explorations Newsletter, 7(2):143–144, ACM Press 2005. 

Tight upper bounds on the number of candidate patterns.
F. Geerts, B. Goethals, and J. Van den Bussche.
ACM Transactions on Database Systems, 30(2):333–363, 2005. 
2004 

Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI 2004).
J. Bayardo, B. Goethals, and M. Zaki (Eds.).
Vol. 126 of CEUR Workshop Proceedings. CEURWS.org 2004. 

Advances in Frequent Itemset Mining Implementations: Report of FIMI'03.
B. Goethals, and M. Zaki.
ACM SIGKDD Explorations, 6(1):109–117, ACM June 2004. 

FPBonsai: the Art of Growing and Pruning Small FPtrees.
F. Bonchi, and B. Goethals.
In Proceedings of the Eighth PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD'04), Vol. 3056 of LNCS, pages 155–160. Springer 2004. 

Tiling Databases.
F. Geerts, B. Goethals, and T. Mielikäinen.
In Discovery Science, Vol. 3245 of Lecture Notes in Computer Science, pages 278–289. Springer 2004. 

On Private Scalar Product Computation for PrivacyPreserving Data Mining.
B. Goethals, S. Laur, H. Lipmaa, and T. Mielikäinen.
In ICISC, Vol. 3506 of Lecture Notes in Computer Science, pages 104–120. Springer 2004. 

Proceedings of the Third International Workshop on Knowledge Discovery in Inductive Databases (KDID 2004) Revised Selected and Invited Papers.
B. Goethals, and A. Siebes (Eds.).
Vol. 3377 of Lecture Notes in Computer Science. Springer 2004. 

Memory issues in frequent itemset mining.
B. Goethals.
In Proceedings of the 2004 ACM Symposium on Applied Computing (SAC'04), pages 530–534. ACM 2004. 
2003 

Minimal kFree Representations of Frequent Sets.
T. Calders, and B. Goethals.
In Proceedings of the Int. Conf. Principles of Data Mining and Knowledge Discovery (PKDD), pages 71–82. 2003. 

Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations (FIMI'03).
B. Goethals, and M. Zaki (Eds.).
Vol. 90 of CEUR Workshop Proceedings. CEURWS.org 2003. 

Advances in Frequent Itemset Mining Implementations: Introduction to FIMI03.
B. Goethals, and M. Zaki.
In Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations (FIMI'03). 

Survey on Frequent Pattern Mining.
B. Goethals.


What You Store is What You Get.
F. Geerts, B. Goethals, and T. Mielikäinen.
In Proceedings of the Second International Workshop on Inductive Databases, pages 6069. 2003. 
2002 

Mining All NonDerivable Frequent Itemsets.
T. Calders, and B. Goethals.
In Principles of Data Mining and Knowledge Discovery, Vol. 2431 of Lecture Notes in Computer Science, pages 74–85. Springer 2002. 

Efficient Frequent Pattern Mining.
B. Goethals.
PhD thesis (transnationale Universiteit Limburg);


Relational Association Rules: getting WARMeR.
B. Goethals, and J. Van den Bussche.
In Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery in Data Mining, Vol. 2447 of LNCS, pages 125–139. SpringerVerlag 2002. 
2001 

A tight upper bound on the number of candidate patterns.
F. Geerts, B. Goethals, and J. Van den Bussche.
In Proceedings of the 2001 IEEE International Conference on Data Mining, pages 155–162. IEEE Computer Society 2001. 
2000 

On Supporting interactive association rule mining.
B. Goethals, and J. Van den Bussche.
In Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery, Vol. 1874 of Lecture Notes in Computer Science, pages 307–316. Springer 2000. 

A Data Mining Framework for Optimal Product Selection in Retail Supermarket Data: The Generalized PROFSET Model.
T. Brijs, B. Goethals, G. Swinnen, K. Vanhoof, and G. Wets.
In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 300–304. ACM Press 2000. 
1999 

A priori versus a posteriori filtering of association rules.
B. Goethals, and J. Van den Bussche.
In Proceedings of the SIGMOD'99 Workshop on Research Issues on Data Mining and Knowledge Discovery. 1999. 
1998 

Decision support queries for the interpretation of data mining results.
B. Goethals, J. Van den Bussche, and K. Vanhoof.
