| 2020 |
|---|
 
|
Bayesian Network based Predictions of Business Processes.
Stephen Pauwels, and Toon Calders.
In Proceedings of the BPM Forum, September 2020. |
| 2019 |
|---|
|
|
Detecting Anomalies in Hybrid Business Process Logs.
Stephen Pauwels, and Toon Calders.
Applied Computing Review, 19(2):18–30, ACM SIGAPP 2019. |
|
|
An Anomaly Detection Technique for Business Processes based on Extended Dynamic Bayesian Networks.
Stephen Pauwels, and Toon Calders.
In Proceedings of the ACM SAC Conference, April 2019. |
|
|
ACD2: a tool to interactively explore Business Process Logs.
Stephen Pauwels, and Toon Calders.
In CEUR workshop proceedings, 2019. |
| 2018 |
|---|
|
|
Introduction to the special issue on discovery science.
Michelangelo Ceci and
Toon Calders.
Machine Learning, 107(11):1647–1649, 2018. |
|
|
PROMETHEE is not quadratic: An O (qnlog (n)) algorithm.
Toon Calders, and Dimitri Van Assche.
Omega, 76:63–69, Elsevier 2018. |
|
|
A novel hierarchical-based framework for upper bound computation of graph edit distance.
Karam Gouda, Mona Arafa, and Toon Calders.
Pattern Recognition, 80:210–224, Elsevier 2018. |
|
|
2SCENT: An Efficient Algorithm for Enumerating All Simple Temporal Cycles (Full version)
Rohit Kumar, and Toon Calders
Technical Report of Github Repository |
|
|
2SCENT: an efficient algorithm to enumerate all simple temporal cycles.
Rohit Kumar, and Toon Calders.
Proceedings of the VLDB Endowment, 11(11):1441–1453, 2018. |
|
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Detecting and Explaining Drifts in Yearly Grant Applications
Stephen Pauwels, and Calders Toon
Technical Report of BPI Challenge 2018 |
|
|
Predicting visitors using location-based social networks.
Muhammad Aamir Saleem, Felipe Soares Da Costa, Peter Dolog, Panagiotis Karras, Torben Bach Pedersen, and Toon Calders.
In 2018 19th IEEE International Conference on Mobile Data Management (MDM), 2018. |
|
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Effective and efficient location influence mining in location-based social networks.
Muhammad Aamir Saleem, Rohit Kumar, Toon Calders, and Torben Bach Pedersen.
Knowledge and Information Systems:1–36, Springer 2018. |
| 2017 |
|---|
|
|
Risk detection and prediction from indoor tracking data.
Tanvir Ahmed, Toon Calders, Hua Lu, and Torben Bach Pedersen.
Sigspatial Special, 9(2):11–18, ACM 2017. |
|
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DS-Prox: Dataset Proximity Mining for Governing the Data Lake.
Ayman Alserafi, Toon Calders, Alberto Abell\'o, and Oscar Romero.
In International Conference on Similarity Search and Applications, pages 284–299, 2017. |
|
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Data mining, social networks and ethical implications.
Toon Calders.
In Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning, 2017. |
|
|
Three Big Data Tools for a Data Scientist’s Toolbox.
Toon Calders.
In European Business Intelligence and Big Data Summer School, pages 112–133, 2017. |
|
|
Cost Model for Pregel on GraphX.
Rohit Kumar, Alberto Abell\'o, and Toon Calders.
In Advances in Databases and Information Systems, pages 153–166, 2017. |
|
|
Activity-Driven Influence Maximization in Social Networks.
Rohit Kumar, Muhammad Aamir Saleem, Toon Calders, Xike Xie, and Torben Bach Pedersen.
In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 345–348, 2017. |
|
|
Information Propagation in Interaction Networks.
Rohit Kumar and
Toon Calders.
In Proceedings of the 20th International Conference on Extending Database Technology (EDBT), Venice, Italy, March, 2017. |
|
|
Finding simple temporal cycles in an interaction network.
Rohit Kumar, and Toon Calders.
In TD-LSG@ PKDD/ECML, Skopje, Macedonia, pages 3–6, 2017. |
|
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IMaxer: A Unified System for Evaluating Influence Maximization in Location-based Social Networks.
Muhammad Aamir Saleem, Rohit Kumar, Toon Calders, Xike Xie, and Torben Bach Pedersen.
In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 2523–2526, 2017. |
|
|
Location Influence in Location-based Social Networks.
Muhammad Aamir Saleem and
Rohit Kumar and
Toon Calders and
Xike Xie and
Torben Bach Pedersen.
In Proceedings of the Tenth ACM International Conference on Web Search
and Data Mining, WSDM 2017, Cambridge, United Kingdom, February
6-10, 2017, pages 621–630, 2017. |
| 2016 |
|---|
|
|
PROMETHEE is Not Quadratic: An O(qn log(n)) Algorithm
Toon Calders and
Dimitri Van Assche
Technical Report of Computing Research Repository (CoRR) (abs/1603.00091);
|
|
|
Discovery Science - 19th International Conference, DS 2016, Bari,
Italy, October 19-21, 2016, Proceedings.
Toon Calders and
Michelangelo Ceci and
Donato Malerba (Eds.).
Vol. 9956 of Lecture Notes in Computer Science |
|
|
Towards information profiling: data lake content metadata management.
Ayman Alserafi, Alberto Abell\'o, Oscar Romero, and Toon Calders.
In Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on, pages 178–185, 2016. |
|
|
Fairness-Aware Data Mining.
Toon Calders.
In 16ème Journèes Francophones Extraction et Gestion
des Connaissances, EGC 2016, 18-22 Janvier 2016, Reims, France, pages 3–4, 2016. |
|
|
BFST\_ED: A Novel Upper Bound Computation Framework for the Graph
Edit Distance.
Karam Gouda and
Mona Arafa and
Toon Calders.
In Similarity Search and Applications - 9th International Conference,
SISAP 2016, Tokyo, Japan, October 24-26, 2016. Proceedings, pages 3–19, 2016. |
|
|
Distributed convoy pattern mining.
Faisal Orakzai, Toon Calders, and Torben Bach Pedersen.
In Mobile Data Management (MDM), 2016 17th IEEE International Conference onVol. 1, pages 122–131, 2016. |
|
|
Mining multi-dimensional complex log data.
Stephen Pauwels, and Toon Calders.
In Proceedings BENELEARN Belgian-Dutch Conference on Machine Learning, 2016. |
|
|
H-WorD: Supporting Job Scheduling in Hadoop with Workload-Driven Data
Redistribution.
Petar Jovanovic and
Oscar Romero and
Toon Calders and
Alberto Abello.
In Advances in Databases and Information Systems - 20th East European
Conference, ADBIS 2016, Prague, Czech Republic, August 28-31, 2016,
Proceedings, pages 306–320, 2016. |
|
|
Online Risk Prediction for Indoor Moving Objects.
Tanvir Ahmed and
Torben Bach Pedersen and
Toon Calders and
Hua Lu.
In IEEE 17th International Conference on Mobile Data Management, MDM
2016, Porto, Portugal, June 13-16, 2016, pages 102–111, 2016. |
| 2015 |
|---|
|
|
Towards population reconstruction: extraction of family relationships from historical documents.
Julia Efremova, Alejandro Montes Garcia, Jianpeng Zhang, and Toon Calders.
In Proc. First ACM SIGKDD Workshop on Population Informatics for Big Data (PopInfo'15), 2015. |
|
|
HiDER: Query-Driven Entity Resolution for Historical Data.
Bijan Ranjbar Sahraei and
Julia Efremova and
Hossein Rahmani and
Toon Calders and
Karl Tuyls and
Gerhard Weiss.
In Machine Learning and Knowledge Discovery in Databases - European Conference,
ECMLPKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings,
Part IIIVol. 9285, pages 281–284, 2015 Springer. |
|
|
Extraction of family relationships from historical documents.
Julia, Efremova, and Toon Calders.
In Dutch-Belgian Database Day 2015 (DBDBD 2015), 2015. |
|
|
Effects of Evolutionary Linguistics in Text Classification.
Julia Efremova and
Alejandro Montes Garcia and
Jianpeng Zhang and
Toon Calders.
In Statistical Language and Speech Processing - Third International Conference,
SLSP 2015, Budapest, Hungary, November 24-26, 2015, Proceedings, pages 50–61, 2015 Springer. |
|
|
Multi-Source Entity Resolution for Genealogical Data.
Julia Efremova and
Bijan Ranjbar Sahraei and
Hossein Rahmani and
Frans A. Oliehoek and
Toon Calders and
Karl Tuyls and
Gerhard Weiss.
Handbuch Fahrerassistenzsysteme, Grundlagen, Komponenten und Systeme
fur aktive Sicherheit und Komfort, pages 129–154, 2015. Springer. |
|
|
Classification of Historical Notary Acts with Noisy Labels.
Julia Efremova and
Alejandro Montes Garcia and
Toon Calders.
In Advances in Information Retrieval - 37th European Conference on IR
Research, ECIR 2015, Vienna, Austria, March 29 - April 2, 2015.
ProceedingsVol. 9022, pages 49–54, 2015. |
|
|
On measuring similarity for sequences of itemsets.
Elias Egho and
Chedy Raissi and
Toon Calders and
Nicolas Jay and
Amedeo Napoli.
Data Mining and Knowledge Discovery, 29(3):732–764, 2015. |
|
|
Towards Distributed Convoy Pattern Mining
Faisal Orakzai and
Thomas Devogele and
Toon Calders
Technical Report of Computing Research Repository (CoRR) (abs/1512.08150);
|
|
|
Towards Distributed Convoy Pattern Mining.
Faisal Orakzai, Thomas Devogele, and Toon Calders.
In Proc. 23rd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 50:1–50:4, 2015 ACM press. |
|
|
Who Are My Ancestors? Retrieving Family Relationships from Historical
Texts.
Julia Efremova and
Alejandro Montes Garcia and
Alfredo Bolt Iriondo and
Toon Calders.
In RuSSIRVol. 573, pages 121–129, 2015 Springer. |
|
|
Maintaining Sliding-Window Neighborhood Profiles in Interaction Networks.
Rohit Kumar and
Toon Calders and
Aristides Gionis and
Nikolaj Tatti.
In Machine Learning and Knowledge Discovery in Databases - European Conference,
ECMLPKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings,
Part IIVol. 9285, pages 719–735, 2015 Springer. |
|
|
Mining Risk Factors in RFID Baggage Tracking Data.
Tanvir Ahmed and
Toon Calders and
Torben Bach Pedersen.
In 16th IEEE International Conference on Mobile Data Management, MDM
2015, Pittsburgh, PA, USA, June 15-18, 2015 - Volume 1, pages 235–242, 2015 IEEE Computer Society. |
|
|
Actes des 11es journees francophones sur les Entrepots
de Donnees et l'Analyse en Ligne, EDA 2015, Bruxelles,
Belgique, 2-3 avril 2015.
Esteban Zimanyi and
Stijn Vansummeren and
Toon Calders (Eds.).
Vol. B-11 of RNTI |
| 2014 |
|---|
|
|
Mining Frequent Itemsets in a Stream.
Toon Calders, Nele Dexters, Joris Gillis, and Bart Goethals.
Informations Systems(39):233-255, Elsevier 2014. |
|
|
Introduction to Pattern Mining.
Toon Calders.
In Business Intelligence - Third European Summer School, eBISS 2013,
Dagstuhl Castle, Germany, July 7-12, 2013, Tutorial LecturesVol. 172, pages 1–32, 2014 Springer. |
|
|
Single-Graph Support Measures.
Toon Calders, Jan Ramon, and Dries Van Dyck.
Quantitative Graph Theory: Mathematical Foundations and Applications, pages 303-324, 2014. CRC Press. |
|
|
Guest Editors' introduction: special issue of the ECML/PKDD 2014
journal track.
Toon Calders and
Floriana Esposito and
Eyke Huellermeier and
Rosa Meo.
Machine Learning, 97(1-2):1–3, 2014. |
|
|
Proceedings of Machine Learning and Knowledge Discovery in Databases - European Conference,
ECMLPKDD 2014, Nancy, France, September 15-19, 2014..
Toon Calders and
Floriana Esposito and
Eyke Huellermeier and
Rosa Meo (Eds.).
Vol. 8724 of Lecture Notes in Computer Science |
|
|
A Hybrid Disambiguation Measure for Inaccurate Cultural Heritage Data.
Julia Efremova, Bijan Ranjbar-Sahraei, and Toon Calders.
In Proceedings of the 8th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities (LaTeCH), pages 47–55, April 2014 Association for Computational Linguistics. |
|
|
A baseline method for genealogical entity resolution.
Julia Efremova, Bijan Ranjbar-Sahraei, Frans Oliehoek, Toon Calders, and Karl Tuyls.
In Workshop on Population Reconstruction, 2014. |
|
|
Guest editors' introduction: special issue of the ECML/PKDD 2014
journal track.
Toon Calders and
Floriana Esposito and
Eyke Huellermeier and
Rosa Meo.
Data Mining and Knowledge Discovery, 28(5-6):1129–1133, 2014. |
|
|
Decomposing a sequence into independent subsequences using compression algorithms.
Hoang Thanh Lam, Julia Kiseleva, Mykola Pechenizkiy, and Toon Calders.
In Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytic, pages 67–75, 2014. |
|
|
Mining Compressing Sequential Patterns.
Hoang Thanh Lam and
Fabian Moerchen and
Dmitriy Fradkin and
Toon Calders.
Statistical Analysis and Data Mining, 7(1):34–52, 2014. |
|
|
Finding Robust Itemsets under Subsampling.
Nikolaj Tatti and
Fabian Moerchen and
Toon Calders.
ACM Transactions on Database Systems, 39(3):20:1–20:27, 2014. |
| 2013 |
|---|
|
|
What Is Data Mining and How Does It Work?.
Toon Calders and
Bart Custers.
Discrimination and Privacy in the Information SocietyVol. 3, pages 27-42, 2013. Springer. |
|
|
Controlling Attribute Effect in Linear Regression.
Toon Calders, Asim Karim, Faisal Kamiran, Wasif Ali, and Xiangliang Zhang.
In Proc. IEEE Int. Conf. on Data Mining, pages 71–80, 2013 IEEE. |
|
|
Quantifying explainable discrimination and removing illegal
discrimination in automated decision making.
Faisal Kamiran and
Indre Zliobaite and
Toon Calders.
Knowledge and Information Systems (KAIS), 35(3):613-644, 2013. |
|
|
Predicting Current User Intent with Contextual Markov Models.
Julia Kiseleva and
Hoang Thanh Lam and
Mykola Pechenizkiy and
Toon Calders.
In 13th IEEE International Conference on Data Mining Workshops, ICDM
Workshops, TX, USA, December 7-10, 2013, pages 391–398, 2013 IEEE Computer Society. |
|
|
Discovering temporal hidden contexts in web sessions for user trail prediction.
Julia Kiseleva, Hoang Thanh Lam, Mykola Pechenizkiy, and Toon Calders.
In Proceedings of the 22nd international conference on World Wide Web, (Companion Volume, TempWeb@WWW'2013 ), pages 1067–1074, 2013 ACM. |
|
|
Introducing Positive Discrimination in Predictive Models.
Sicco Verwer and
Toon Calders.
Discrimination and Privacy in the Information SocietyVol. 3, pages 255-270, 2013. Springer. |
|
|
Analysis of videos using tile mining.
Toon Calders, Elisa Fromont, Baptiste Jeudy, and Hoang Thanh Lam.
In Proceedings of the ECML/PKDD Woskshop on Real-World Challenges for Data Stream Mining, 2013. |
|
|
Why Unbiased Computational Processes Can Lead to Discriminative
Decision Procedures.
Toon Calders and
Indre Zliobaite.
Discrimination and Privacy in the Information SocietyVol. 3, pages 43-57, 2013. Springer. |
|
|
Extraction des k plus grandes tuiles dans un flux de donnees.
Toon Calders, Elisa Fromont, Baptiste Jeudy, Hoang Thanh Lam, Wenjie Pei, and Adriana Prado.
In Conference Francophone sur l'Apprentissage Automatique, 2013. |
|
|
Techniques for Discrimination-Free Predictive Models.
Faisal Kamiran and
Toon Calders and
Mykola Pechenizkiy.
Discrimination and Privacy in the Information SocietyVol. 3, pages 223-239, 2013. Springer. |
|
|
An interactive, web-based tool for genealogical entity resolution.
Julia Efremova, Bijan Ranjbar-Sahraei, Frans A Oliehoek, Toon Calders, and Karl Tuyls.
In 25th Benelux Conference on Artificial Intelligence, pages 376–377, 2013. |
|
|
Vers une mesure de similarite pour les séquences
complexes.
Elias Egho and
Chedy Raissi and
Toon Calders and
Thomas Bourquard and
Nicolas Jay and
Amedeo Napoli.
In Extraction et gestion des connaissances (EGC'2013), pages 335-340, 2013. |
|
|
The Way Forward.
Bart Custers and
Toon Calders and
Tal Z. Zarsky and
Bart Schermer.
Discrimination and Privacy in the Information SocietyVol. 3, pages 341-357, 2013. Springer. |
|
|
Zips: mining compressing sequential patterns in streams.
Hoang Thanh Lam, Toon Calders, Jie Yang, Fabian Mörchen, and Dmitriy Fradkin.
In Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics, pages 54–62, 2013. |
| 2012 |
|---|
|
|
Data preprocessing techniques for classification without discrimination.
F. Kamiran, and T. Calders.
Knowledge and Information Systems (KAIS), 33(1):1–33, Springer 2012. |
|
|
Mining Compressing Sequential Patterns.
T.L. Hoang, F. Moerchen, D. Fradkin, and T. Calders.
In Proc. SIAM Int. Conf. on Data Mining, pages 319–330, 2012. |
|
|
Recent Developments in Pattern Mining.
Toon Calders.
In Algorithmic Learning Theory - 23rd International Conference,
ALT 2012, Lyon, France, October 29-31, 2012. ProceedingsVol. 7568, pages 34, 2012 Springer. |
|
|
Recent Developments in Pattern Mining.
Toon Calders.
In Discovery ScienceVol. 7569, pages 2, 2012 Springer. |
|
|
Technologies for dealing with information overload: An engineer's point of view.
Toon Calders, George HL Fletcher, Faisal Kamiran, and Mykola Pechenizkiy.
Information overload: an international challenge for professional engineers and technical communicators, pages 175–202, 2012. Wiley Online Library. |
|
|
An Inductive Database System Based on Virtual Mining Views.
Hendrik Blockeel, Toon Calders, Elisa Fromont, Bart Goethals, Adriana Prado, and Celine Robardet.
Data Mining and Knowledge Discovery, 24(1):247-287, Springer 2012. |
| 2011 |
|---|
|
|
All normalized anti-monotonic overlap graph measures are
bounded.
T. Calders and
J. Ramon and
D. Van Dyck.
Data Mining and Knowledge Discovery, 23(3):503-548, 2011. |
|
|
Proceedings of the 4th International Conference on Educational
Data Mining, Eindhoven, The Netherlands, July 6-8, 2011.
J. C. M. Pechenizkiy and
T. Calders and
C. Conati and
S. Ventura and
C. Romero and
Stamper (Eds.).
|
|
|
Handling Conditional Discrimination.
I. Zliobaite and
F. Kamiran and
T. Calders.
In Proc. IEEE Int. Conf. on Data Mining, pages 992-1001, 2011. |
|
|
Introduction to the special section on educational data mining.
Toon Calders and
Mykola Pechenizkiy.
SIGKDD Explorations, 13(2):3–6, 2011. |
|
|
Online Discovery of Top-k Similar Motifs in Time Series
Data.
T.L. and
T. Calders and
Pham, N. Hoang.
In Proc. SIAM Int. Conf. on Data Mining, pages 1004-1015, 2011. |
|
|
Big data mining, fairness and privacy.
Dino Pedreschi, Toon Calders, BHM Custers, Josep Domingo-Ferrer, Giusella Finocchiaro, and others.
Privacy Observatory Magazine, 2011. |
| 2010 |
|---|
|
|
Efficient Pattern Mining from Uncertain Data with Sampling.
Toon Calders, Calin Garboni, and Bart Goethals.
In Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2010), 2010 Springer. |
|
|
Chapter 7: Association Rule Mining in Learning Management Systems.
E. Garcia, C. Romero, S. Ventura, S. de Castro, and T. Calders.
Handbook of Educational Data Mining, 2010. CRC Press. |
|
|
InfraWatch: Data Management of Large Systems for Monitoring
Infrastructural Performance.
A.J. and
H. Blockeel and
A. Koopman and
T. Calders and
B. Obladen and
C. Bosma and
H. Galenkamp and
E. Koenders and
Kok, J.N. Knobbe.
In Advances in Intelligent Data Analysis IX, 9th International
Symposium, IDA 2010, Tucson, AZ, USA, May 19-21, 2010. Proceedings, pages 91-102, 2010. |
|
|
Mining top-k frequent items in a data stream with flexible
sliding windows.
T.L. and
T. Calders Hoang.
In Proc. KDD Int. Conf. Knowledge Discovery in Databases, pages 283-292, 2010. |
|
|
Inductive querying with virtual mining views.
Hendrik Blockeel, Toon Calders, Elisa Fromont, Bart Goethals, Adriana Prado, and Celine Robardet.
In Inductive Databases and Queries: Constraint-based Data Mining, pages 265–287, 2010 Springer. |
|
|
A practical comparative study of data mining query languages.
Hendrik Blockeel, Toon Calders, Elisa Fromont, Bart Goethals, Adriana Prado, and Céline Robardet.
Inductive Databases and Constraint-Based Data Mining, pages 59–77, 2010. Springer. |
|
|
Inductive querying with virtual mining views.
Hendrik Blockeel, Toon Calders, Elisa Fromont, Bart Goethals, Adriana Prado, and Celine Robardet.
Inductive Databases and Queries: Constraint-based Data Mining, pages 265–287, 2010. Springer. |
|
|
Approximating Frequentness Probability of Itemsets in Uncertain Data.
Toon Calders, Calin Garboni, and Bart Goethals.
In Proceedings of the 10th IEEE International Conference on Data Mining (ICDM-2010), 2010. |
|
|
Three naive Bayes approaches for discrimination-free classification.
T. Calders and
S. Verwer.
Data Mining and Knowledge Discovery, 21(2):277-292, 2010. |
|
|
Discrimination Aware Decision Tree Learning
F. Kamiran, T. Calders, and M. Pechenizkiy
Technical Report of Eindhoven University of Technology, Dept. Math. and Computer Science (CS-Report 10-13);
|
|
|
Discrimination Aware Decision Tree Learning.
F. Kamiran and
T. Calders and
M. Pechenizkiy.
In Proc. IEEE Int. Conf. on Data Mining, pages 869-874, 2010. |
| 2009 |
|---|
|
|
Using the Minimum Description Length Principle to Evaluate Process Models.
T. Calders, C. Güenther, A. Rozinat, and M. Pechenizkiy.
In ACM Symposium on Applied Computing, Data Mining Track (ACM SAC-DM), pages 1451–1455, 2009. |
|
|
Building Classifiers with Independency Constraints.
T. Calders and
F. Kamiran and
M. Pechenizkiy.
In ICDM Workshops, pages 13-18, 2009. |
|
|
Classification Without Discrimination.
F. Kamiran, and T. Calders.
In IEEE International Conference on Computer, Control & Communication (IEEE-IC4), 2009 IEEE press. |
|
|
Proceedings of the 21st Benelux conference on Artificial Intelligence.
T. Calders, K. Tuyls, and M. Pechenizkiy (Eds.).
2009. |
| 2008 |
|---|
|
|
Anti-Monotonic Overlap-Graph Support Measures.
T. Calders, J. Ramon, and D. Van Dyck.
In International Conference on Data Mining (ICDM), pages 73–82, 2008 IEEE. |
|
|
The Complexity of Satisfying Constraints on Transaction Databases.
T. Calders.
Accepted September 2007 for publication in Acta Informatica, to appear, 2008. |
|
|
Itemset Frequency Satisfiability: Complexity and Axiomatization.
T. Calders.
Accepted November 2007 for publication in Theoretical computer Science, to appear, 2008. |
|
|
Min, Max and PTIME Anti-Monotonic Overlap Graph Measures.
T. Calders, J. Ramon, and D. Van Dyck.
In 6th International Workshop on Mining and Learning with Graphs (MLG), 2008. |
|
|
Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study.
M. Pechenizkiy, T. Calders, E. Vasilyeva, and P. De Bra.
In 1st International Conference on Educational Data Mining (EDM2008), pages 187–191, 2008. |
|
|
Mining conjunctive sequential patterns.
C. Raissi, T. Calders, and P. Poncelet.
Data Mining and Knowledge Discovery, 17(1):77-93, Springer August 2008. |
|
|
Mining conjunctive sequential patterns: Extended Abstract.
C. Raissi, T. Calders, and P. Poncelet.
In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 19, 2008. |
|
|
Itemset Frequency Satisfiability: Complexity and Axiomatization.
T. Calders.
Theoretical Computer Science, 394(1-2):84-111, Elsevier 2008. |
|
|
Mining Frequent Items in a Stream using Flexible Windows.
Toon Calders, Nele Dexters, and Bart Goethals.
Intelligent Data Analysis, 12(3), iospress 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), 2008. |
|
|
Mining Views: Database Views for Data Mining.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, and A. Prado.
In Proc. IEEE ICDE, 2008. |
| 2007 |
|---|
|
|
Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML-2007).
Cristobal Romero, Mykola Pechenizkiy, Toon Calders, Joseph E. Beck, and Frans Van Assche (Eds.).
Vol. 305 |
|
|
Workshop on Educational Data Mining @ ICALT07 (EDM@ICALT07).
Joseph E. Beck, Toon Calders, Mykola Pechenizkiy, and Silvia Rita Viola.
In Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007), pages 933–934, 2007. |
|
|
Non-Derivable 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. |
|
|
A Framework for Guiding the Museum Tour Personalization.
M. Pechenizkiy, and T. Calders.
In Proceedings UM 2007 International Workshop on Personalization Enhanced Access to Cultural Heritage (CHIP), 2007. |
|
|
Efficient AUC-Optimization for Classification.
T. Calders, and S. Jaroszewicz.
In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, 2007 Springer. |
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Association rule mining in learning management systems: drawbacks and solutions.
Enrique Garcia, Cristobal Romero, Sebastian Ventura, and Toon Calders.
In Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML’07) in conjunction with the Second European Conference on
Technology Enhanced Learning (EC-TEL07), 2007. |
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Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML-2007).
C. Romero, M. Pechenizkiy, T. Calders, J. E. Beck, and F. Van Assche (Eds.).
Vol. 305 |
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The Complexity of Satisfying Constraints on Transaction Databases.
T. Calders.
Acta Informatica, 44(7-8):591-624, Springer 2007. |
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Mining Views: Database Views for Data Mining.
H. Blockeel, T. Calders, E. Fromont, B. Goethals, and A. Prado.
In ECML/PKDD-2007 International Workshop on Constraint-Based Mining and Learning (CMILE), 2007. |
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Mining frequent itemsets in a stream.
T. Calders, N. Dexters, and B. Goethals.
In Proc. IEEE Int. Conf. on Data Mining, pages 83–92, 2007. |
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Mining itemsets in the presence of missing values.
Toon Calders, Bart Goethals, and Michael Mampaey.
In Proceedings of the ACM Symposium on Applied Computing, pages 404–408, 2007 ACM. |
| 2006 |
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A Survey on Condensed Representations for Frequent Sets.
T. Calders, C. Rigotti, and J-F. Boulicaut.
Constraint-Based MiningVol. 3848, 2006. Springer. |
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Mining Frequent Items in a Stream Using Flexible Windows.
T. Calders, N. Dexters, and B. Goethals.
In ECML/PKDD-2006 International Workshop on Knowledge Discovery from Data Streams (IWKDDS), 2006. |
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Integrating Pattern Mining in Relational Databases.
T. Calders, B. Goethals, and A. B. Prado.
In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, 2006 Springer. |
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Expressive power of an algebra for data mining.
T. Calders, L. V.S. Lakshmanan, R. T. Ng, and J. Paredaens.
ACM Trans. on Database Systems, 31(4):1169–1214, ACM Press 2006. |
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Analyzing workflows implied by instance-dependent access rules.
T. Calders, S. Dekeyser, J. Hidders, and J. Paredaens.
In Proc. of the 25th ACM SIGACT-SIGMOD-SIGART Symposium on Priciples of Database Systems (PODS 2006), pages 100–109, 2006 ACM Press. |
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Constraint Extraction from SQL-queries (manuscript)
T. Calders, B. Goethals, and A. B. Prado.
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Mining Rank-Correlated Sets of Numerical Attributes.
Toon Calders, Bart Goethals, and Szymon Jaroszewicz.
In 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2006. |
| 2005 |
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Applying Webmining Techniques to Execution Traces to Support the Program Comprehension Process.
A. Zaidman, T. Calders, S. Demeyer, and J. Paredaens.
In 9th European Conference on Software Maintenance and Reengineering (CSMR), 2005. |
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Quick Inclusion-Exclusion.
T. Calders, and B. Goethals.
In Proceedings ECML-PKDD 2005 Workshop Knowledge Discovery in Inductive DatabasesVol. 3933, 2005 Springer. |
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Depth-first non-derivable itemset mining.
T. Calders, and B. Goethals.
In Proc. SIAM Int. Conf. on Data Mining, 2005. |
| 2004 |
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Selective Introduction of Aspects for Program Comprehension.
A. Zaidman, T. Calders, S. Demeyer, and J. Paredaens.
In WCRE Workshop on Aspect Reverse Engineering (WARE), 2004. |
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Deducing Bounds on the Support of Itemsets..
T. Calders.
Database Support for Data Mining ApplicationsVol. 2682, pages 214-233, 2004. Springer. |
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Computational Complexity of Itemset Frequency Satisfiability.
T. Calders.
In Proc. PODS Int. Conf. Principles of Database Systems, pages 143-154, 2004. |
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A Formal Framework for Evaluation of Information Extraction
A. Desitter, T. Calders, and W. Daelemans
Technical Report of Universitaire Instelling Antwerpen, Department of Mathematics & Computer Science (2004-04);
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Theoretical bounds on the size of condensed representations.
N. Dexters, and T. Calders.
In Proceedings ECML-PKDD 2004 Workshop Knowledge Discovery in Inductive Databases, pages 25-36, 2004. |
| 2003 |
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Axiomatization and Deduction Rules for the Frequency of Itemsets
T. Calders
PhD thesis (University of Antwerp, Belgium);
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Minimal k-Free Representations of Frequent Sets.
T. Calders, and B. Goethals.
In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 71–82, 2003. |
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Axiomatization of Frequent Itemsets.
T. Calders, and J. Paredaens.
Theoretical Computer Science, 290(1):669–693, 2003. |
| 2002 |
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Deducing Bounds on the Frequency of Itemsets.
T. Calders.
In EDBT Workshop DTDM Database Techniques in Data Mining, 2002. |
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Mining All Non-Derivable Frequent Itemsets.
T. Calders, and B. Goethals.
In Proc. PKDD Int. Conf. Principles of Data Mining and Knowledge Discovery, pages 74–85, 2002 Springer. |
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Searching for Dependencies at Multiple Abstraction Levels.
T. Calders, J. Wijsen, and R. T. Ng.
ACM Trans. on Database Systems, 27(3):229–260, 2002. |
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Mining All Non-Derivable Frequent Itemsets
Toon Calders and
Bart Goethals
Technical Report of Computing Research Repository (CoRR) (cs.DB/0206004);
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| 2001 |
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On Monotone Data Mining Langauages.
T. Calders, and J. Wijsen.
In Proc. DBPL Workshop on Databases and Programming Languages, pages 119–132, 2001. |
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Axiomatization of Frequent Sets.
T. Calders, and J. Paredaens.
In Proc. ICDT Int. Conf. Database Theory, pages 204–218, 2001. |
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On Monotone Data Mining Languages
T. Calders, and J. Wijsen
Technical Report of Universitaire Instelling Antwerpen, Department of Mathematics & Computer Science (2001-08);
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| 2000 |
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Mining Frequent Binary Expressions.
T. Calders, and J. Paredaens.
In Proc. DaWaK Int. Conf. Data Warehousing and Knowledge Discovery, pages 399–408, 2000. |
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A Theoretical Framework for Reasoning about Frequent Itemsets
T. Calders, and J. Paredaens
Technical Report of University of Antwerp, Dept. Math. & Computer Science (2000-06);
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Mining Binary Expressions: Applications and Algorithms
T. Calders, and J. Paredaens
Technical Report of University of Antwerp, Dept. Math. & Computer Science (2000-08);
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| 1999 |
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Het ontdekken van roll-up afhankelijkheden in databases (In Dutch)
T. Calders
Masters thesis (University of Antwerp, Dept. Math. & Computer Science);
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Discovering Roll-Up Dependencies.
J. Wijsen, R.T. Ng, and T. Calders.
In Proc. KDD Int. Conf. Knowledge Discovery in Databases, pages 213–222, 1999. |