Publications
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Publications by Year per type also see DBLP, Google Scholar, or Semantic Scholar

In Press
Marx, A & Vreeken, J Telling Cause from Effect by Local and Global Regression. Knowledge and Information Systems, pp 1-19, IEEE (IF 2.247)
2018
Budhathoki, K & Vreeken, J Accurate Causal Inference on Discrete Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'18), IEEE, 2018 (19.9% acceptance rate).
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'18), IEEE, 2018 (full paper, 8.9% acceptance rate; overall 19.9%). (Best Paper Award)
Marx, A & Vreeken, J Causal Inference on Multivariate and Mixed Type Data. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Data (ECMLPKDD), Springer, 2018 (25% acceptance rate).
Budhathoki, K & Vreeken, J Causal Inference on Event Sequences. In: Proceedings of the SIAM Conference on Data Mining (SDM), pp 55-63, SIAM, 2018 (23.2% acceptance rate).
Budhathoki, K, Boley, M & Vreeken, J Rule Discovery for Exploratory Causal Reasoning. In: Proceedings of the NIPS 2018 workshop on Causal Learning, pp 1-14, 2018.
Marx, A & Vreeken, J Stochastic Complexity for Testing Conditional Independence on Discrete Data. In: Proceedings of the NIPS 2018 workshop on Causal Learning, pp 1-12, 2018.
Wu, H, Ning, Y, Chakraborty, P, Vreeken, J, Tatti, N & Ramakrishnan, N Generating Realistic Synthetic Population Datasets. Transactions on Knowledge Discovery from Data vol.12(4), pp 1-45, ACM, 2018. (IF 1.68)
List, M, Hornakova, A, Vreeken, J & Schulz, MH JAMI — Fast computation of Conditional Mutual Information for ceRNA network analysis. Bioinformatics vol.34(17), pp 3050-3051, Oxford University Press, 2018. (IF 7.307)
Budhathoki, K & Vreeken, J Origo: Causal Inference by Compression. Knowledge and Information Systems vol.56(2), pp 285-307, Springer, 2018. (IF 2.247)
2017
Budhathoki, K & Vreeken, J MDL for Causal Inference on Discrete Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'17), pp 751-756, IEEE, 2017 (19.9% acceptance rate).
Marx, A & Vreeken, J Telling Cause from Effect by MDL-based Local and Global Regression. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'17), pp 307-316, IEEE, 2017 (full paper, 9.3% acceptance rate; overall 19.9%). (invited for the KAIS Special Issue on the Best of IEEE ICDM 2017)
Kalofolias, J, Boley, M & Vreeken, J Efficiently Discovering Locally Exceptional yet Globally Representative Subgroups. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'17), IEEE, 2017 (full paper, 9.3% acceptance rate; overall 19.9%).
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Approximate Functional Dependencies. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp 355-363, ACM, 2017 (oral presentation, 8.6% acceptance rate; overall 17.5%).
Budhathoki, K & Vreeken, J Correlation by Compression. In: Proceedings of the SIAM Conference on Data Mining (SDM), SIAM, 2017 (25% acceptance rate).
Bertens, R, Vreeken, J & Siebes, A Efficiently Discovering Unexpected Pattern-Co-Occurrences. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 126-134, SIAM, 2017 (25% acceptance rate).
Bhattacharyya, A & Vreeken, J Efficiently Summarising Event Sequences with Rich Interleaving Patterns. In: Proceedings of the SIAM Conference on Data Mining (SDM), pp 795-803, SIAM, 2017 (selected in the top 10 papers of SDM'17, 2.7% acceptance rate; overall 25%).
Pienta, R, Kahng, M, Lin, Z, Vreeken, J, Talukdar, P, Abello, J, Parameswaran, G & Chau, DH Adaptive Local Exploration of Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 597-605, SIAM, 2017 (25% acceptance rate).
Hinrichs, F & Vreeken, J Characterising the Difference and the Norm between Sequences Databases. In: Proceedings of the 4th Workshop on Interactions between Data Mining and Natural Language Processing (DMNLP'17), pp 49-64, CEUR Workshop Proceedings, 2017.
Grosse, K & Vreeken, J Summarising Event Sequences using Serial Episodes and an Ontology. In: Proceedings of the 4th Workshop on Interactions between Data Mining and Natural Language Processing (DMNLP'17), pp 33-48, CEUR Workshop Proceedings, 2017.
Boley, M, Goldsmith, BR, Ghiringhelli, LM & Vreeken, J Identifying Consistent Statements about Numerical Data with Dispersion-Corrected Subgroup Discovery. Data Mining and Knowledge Discovery vol.31(5), pp 1391-1418, Springer, 2017. (IF 3.160) (ECML PKDD'17 Journal Track)
Fischer, AK, Vreeken, J & Klakow, D Beyond Pairwise Similarity: Quantifying and Characterizing Linguistic Similarity between Groups of Languages by MDL. Computación y Sistemas vol.21(4), 2017. (Special Issue for the 18th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing'17)
Goldsmith, B, Boley, M, Vreeken, J, Scheffler, M & Ghiringhelli, L Uncovering Structure-Property Relationships of Materials by Subgroup Discovery. New Journal of Physics vol.19, IOP Publishing Ltd and Deutsche Physikalische Gesellschaft, 2017. (IF 3.57) (Included in the NJP Highlights of 2017)
2016
Budhathoki, K & Vreeken, J Causal Inference by Compression. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'16), IEEE, 2016 (full paper, 8.5% acceptance rate; overall 19.6%). (invited for the KAIS Special Issue on the Best of IEEE ICDM 2016)
Bertens, R, Vreeken, J & Siebes, A Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'16), pp 735-744, ACM, 2016 (oral presentation, 8.9% acceptance rate; overall 18.1%).video
Rozenshtein, P, Gionis, A, Prakash, BA & Vreeken, J Reconstructing an Epidemic over Time. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp 1835-1844, ACM, 2016 (18.1% acceptance rate).
Nguyen, H-V & Vreeken, J Flexibly Mining Better Subgroups. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 585-593, SIAM, 2016 (overall 25% acceptance rate).
Nguyen, H-V, Mandros, P & Vreeken, J Universal Dependency Analysis. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 792-800, SIAM, 2016 (overall 25% acceptance rate).
Nguyen, H-V & Vreeken, J Linear-time Detection of Non-Linear Changes in Massively High Dimensional Time Series. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 828-836, SIAM, 2016 (overall 25% acceptance rate).
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). 2016.website
Frasconi, P, Landwehr, N, Manco, G & Vreeken, J (eds) Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD). Springer, 2016. (Part I)website
Frasconi, P, Landwehr, N, Manco, G & Vreeken, J (eds) Proceedings of the European Conference on Machine Learning and Principles and Practices of Knowledge Discovery in Data (ECMLPKDD). Springer, 2016. (Part II)website
Athukorala, K, Glowacka, D, Jacucci, G, Oulasvirta, A & Vreeken, J Is Exploratory Search Different? A Comparison of Information Search Behavior for Exploratory and Lookup Tasks. Journal of the Association for Information Science and Technology (JASIST) vol.67(11), pp 2635-2651, Wiley, 2016. (IF 2.26)
2015
Pienta, R, Lin, Z, Kahng, M, Vreeken, J, Talukdar, PP, Abello, J, Parameswaran, G & Chau, DH AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs. In: Proceedings of the IEEE Conference on Visualization (VIS), IEEE, 2015.video
Budhathoki, K & Vreeken, J The Difference and the Norm – Characterising Similarities and Differences between Databases. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 206-223, Springer, 2015.
Nguyen, H-V & Vreeken, J Non-Parametric Jensen-Shannon Divergence. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 173-189, Springer, 2015.
Vreeken, J Causal Inference by Direction of Information. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 909-917, SIAM, 2015.
Karaev, S, Miettinen, P & Vreeken, J Getting to Know the Unknown Unknowns: Destructive-Noise Resistant Boolean Matrix Factorization. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 325-333, SIAM, 2015.
Sundareisan, S, Vreeken, J & Prakash, BA Hidden Hazards: Finding Missing Nodes in Large Graph Epidemics. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 415-423, SIAM, 2015.
Chau, DH, Vreeken, J, van Leeuwen, M, Shahaf, D & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). 2015.website
Koutra, D, Kang, U, Vreeken, J & Faloutsos, C Summarizing and Understanding Large Graphs. Statistical Analysis and Data Mining vol.8(3), pp 183-202, Wiley, 2015.
Zimek, A & Vreeken, J The Blind Men and the Elephant: About Meeting the Problem of Multiple Truths in Data from Clustering and Pattern Mining Perspectives. Machine Learning vol.98(1), pp 121-155, Springer, 2015. (IF 1.587)
2014
Athukorala, K, Oulasvirta, A, Glowacka, D, Vreeken, J & Jaccuci, G Narrow or Broad? Estimating Subjective Specificity in Exploratory Search. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp 819-828, ACM, 2014 (IR track full paper, overall 21% acceptance rate).
Kuzey, E, Vreeken, J & Weikum, G A Fresh Look on Knowledge Bases: Distilling Named Events from News. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp 1689-1698, ACM, 2014 (KM track full paper, overall 21% acceptance rate).
Nguyen, H-V, Müller, E, Vreeken, J & Böhm, K Multivariate Maximal Correlation Analysis. In: Proceedings of the International Conference on Machine Learning (ICML), pp 775-783, JMLR: W&CP vol.32, 2014 (25.0% acceptance rate).
Koutra, D, Kang, U, Vreeken, J & Faloutsos, C VoG: Summarizing and Understanding Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 91-99, SIAM, 2014. (fast track journal invitation, as one of the best of SDM'14; full paper with presentation, 15.4% acceptance rate)
Vreeken, J & Tatti, N Interesting Patterns. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 105-134, pp 105-134, Springer, 2014.
van Leeuwen, M & Vreeken, J Mining and Using Sets of Patterns through Compression. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 165-198, pp 165-198, Springer, 2014.
Zimek, A, Assent, I & Vreeken, J Frequent Pattern Mining Algorithms for Data Clustering. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 403-424, pp 403-424, Springer, 2014.
Athukorala, K, Oulasvirta, A, Glowacka, D, Vreeken, J & Jacucci, G Supporting Exploratory Search Through User Modeling. In: Proceedings of the UMAP Joint Workshop on Personalized Information Access (PIA), pp 1-6, 2014.
Athukorala, K, Oulasvirta, A, Glowacka, D, Vreeken, J & Jacucci, G Interaction Model to Predict Subjective-Specificity of Search Results. In: Proceedings of the 22nd Conference on User Modeling, Adaptation and Personalization — Late-Breaking Results (UMAP), pp 1-6, 2014.
Gandhi, M & Vreeken, J Slimmer, outsmarting Slim. PhD Poster and Video at: the 13th International Symposium on Intelligent Data Analysis (IDA), Springer, 2014.
video
Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). 2014.website
Miettinen, P & Vreeken, J mdl4bmf: Minimal Description Length for Boolean Matrix Factorization. Transactions on Knowledge Discovery from Data vol.8(4), pp 1-30, ACM, 2014. (IF 1.68)
Wu, H, Vreeken, J, Tatti, N & Ramakrishnan, N Uncovering the Plot: Detecting Surprising Coalitions of Entities in Multi-Relational Schemas. Data Mining and Knowledge Discovery vol.28(5), pp 1398-1428, Springer, 2014. (IF 2.877) (ECML PKDD'14 Journal Track)
Nguyen, H-V, Müller, E, Vreeken, J & Böhm, K Unsupervised Interaction-Preserving Discretization of Multivariate Data. Data Mining and Knowledge Discovery vol.28(5), pp 1366-1397, Springer, 2014. (IF 2.877) (ECML PKDD'14 Journal Track)
Prakash, BA, Vreeken, J & Faloutsos, C Efficiently Spotting the Starting Points of an Epidemic in a Large Graph. Knowledge and Information Systems vol.38(1), pp 35-59, Springer, 2014. (IF 2.225)
Webb, G & Vreeken, J Efficient Discovery of the Most Interesting Associations. Transactions on Knowledge Discovery from Data vol.8(3), pp 1-31, ACM, 2014. (IF 1.68)implementation
2013
Akşehirli, E, Goethals, B, Müller, E & Vreeken, J Cartification: A Neighborhood Preserving Transformation for Mining High Dimensional Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 937-942, IEEE, 2013 (19.6% acceptance rate).website
Ramon, J, Miettinen, P & Vreeken, J Detecting Bicliques in GF[q]. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 509-524, Springer, 2013.
Kontonasios, K-N, Vreeken, J & De Bie, T Maximum Entropy Models for Iteratively Identifying Subjectively Interesting Structure in Real-Valued Data. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 256-271, Springer, 2013.
Nguyen, H-V, Müller, E, Vreeken, J, Keller, F & Böhm, K CMI: An Information-Theoretic Contrast Measure for Enhancing Subspace Cluster and Outlier Detection. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 198-206, SIAM, 2013 (oral presentation, 14.4% acceptance rate; overal 25%).website
Akoglu, L, Vreeken, J, Tong, H, Chau, DH, Tatti, N & Faloutsos, C Mining Connection Pathways for Marked Nodes in Large Graphs. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 37-45, SIAM, 2013 (oral presentation, 14.4% acceptance rate; overal 25%).
Chau, DH, Vreeken, J, van Leeuwen, M & Faloutsos, C (eds) Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics (IDEA). ACM, 2013.website
Mampaey, M & Vreeken, J Summarizing Categorical Data by Clustering Attributes. Data Mining and Knowledge Discovery vol.26(1), pp 130-173, Springer, 2013. (IF 2.877)
2012
Prakash, BA, Vreeken, J & Faloutsos, C Spotting Culprits in Epidemics: How many and Which ones?. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 11-20, IEEE, 2012 (full paper, 10.7% acceptance rate; overall 20%).
Akoglu, L, Tong, H, Vreeken, J & Faloutsos, C Fast and Reliable Anomaly Detection in Categoric Data. In: Proceedings of ACM Conference on Information and Knowledge Management (CIKM), pp 415-424, ACM, 2012 (full paper, 13.4% acceptance rate; 27% overall).
Tatti, N & Vreeken, J Discovering Descriptive Tile Trees by Fast Mining of Optimal Geometric Subtiles. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 9-24, Springer, 2012.
Tatti, N & Vreeken, J The Long and the Short of It: Summarising Event Sequences with Serial Episodes. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 462-470, ACM, 2012 (17.6% acceptance rate).
video
Smets, K & Vreeken, J Slim: Directly Mining Descriptive Patterns. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 236-247, SIAM, 2012. (oral presentation, 14.6% acceptance rate)
Akoglu, L, Vreeken, J, Tong, H, Chau, DH & Faloutsos, C Mining and Visualizing Connection Pathways in Large Information Networks. In: Proceedings of the Workshop on Information in Networks (WIN), pp 1-3, 2012.
Vreeken, J & Tatti, N Summarising Event Sequences with Serial Episodes. In: Proceedings of the 5th Workshop on Information Theoretic Methods in Science and Engineering (WITMSE), pp 82-85, 2012. (invited contribution, extended abstract of our KDD'12 paper)
Wu, H, Mampaey, M, Tatti, N, Vreeken, J, Hossain, MS & Ramakrishnan, N Where Do I Start? Algorithmic Strategies to Guide Intelligence Analysts. In: Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics (ISI-KDD), pp 1-8, ACM, 2012.
Chau, DH, Akoglu, L, Vreeken, J, Tong, H & Faloutsos, C Interactively and Visually Exploring Tours of Marked Nodes in Large Graphs. Demo at, and included in: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), ACM, 2012.
Chau, DH, Akoglu, L, Vreeken, J, Tong, H & Faloutsos, C TourViz: Interactive Visualization of Connection Pathways in Large Graphs. Demo at, and included in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 1516-1519, ACM, 2012.
Vreeken, J, Ling, C, Zaki, MJ, Siebes, A, Yu, JX, Goethals, B, Webb, G & Wu, X (eds) Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2012.website
Vreeken, J, van Leeuwen, M, Nijssen, S, Tatti, N, Dries, A & Goethals, B (eds) Proceedings of the ECML PKDD Workshop on Instant Interactive Data Mining (IID). 2012.website
Mampaey, M, Vreeken, J & Tatti, N Summarizing Data Succinctly with the Most Informative Itemsets. Transactions on Knowledge Discovery from Data vol.6(4), pp 1-44, ACM, 2012. (IF 1.68)
Tatti, N & Vreeken, J Comparing Apples and Oranges – Measuring Differences between Exploratory Data Mining Results. Data Mining and Knowledge Discovery vol.25(2), pp 173-207, Springer, 2012. (IF 1.545) (ECMLPKDD'11 Special Issue)
video
2011
Kontonasios, K-N, Vreeken, J & De Bie, T Maximum Entropy Modelling for Assessing Results on Real-Valued Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 350-359, IEEE, 2011. (oral presentation, 12.3% acceptance rate; overall 18%)
Smets, K & Vreeken, J Identifying and Characterising Anomalies in Transaction Data. In: Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC), ISSN 1568-7805, 2011. (extended abstract of our SDM'11 paper)
Mampaey, M, Tatti, N & Vreeken, J Data Summarization with Informative Itemsets. In: Proceedings of the 23rd Benelux Conference on Artificial Intelligence (BNAIC), ISSN 1568-7805, 2011. (extended abstract of our KDD'11 paper)
Tatti, N & Vreeken, J Comparing Apples and Oranges – Measuring Differences between Data Mining Results. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 398-413, Springer, 2011. (invited for extension for best-of special issue, 3% acceptance rate; overall 20%)
video
Miettinen, P & Vreeken, J Model Order Selection for Boolean Matrix Factorization. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 51-59, ACM, 2011. (oral presentation, 7.8% acceptance rate; overall 17.5%)
Mampaey, M, Tatti, N & Vreeken, J Tell Me What I Need To Know: Succinctly Summarizing Data with Itemsets. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 573-581, ACM, 2011. (Best Student Paper Award; oral presentation, 7.8% acceptance rate; overall 17.5%)
Smets, K & Vreeken, J The Odd One Out: Identifying and Characterising Anomalies. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 804-815, SIAM, 2011. (25% acceptance rate)
Vreeken, J & Zimek, A When Pattern Met Subspace Cluster - A Relationship Story. In: Proceedings of the 2nd Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust), pp 7-18, 2011.
Goethals, B, Moens, S & Vreeken, J mime: A Framework for Interactive Visual Pattern Mining. Demo at, and included in: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 634-637, Springer, 2011.website
Goethals, B, Moens, S & Vreeken, J mime: A Framework for Interactive Visual Pattern Mining. Demo at, and included in: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp 757-760, ACM, 2011.website
Remmerie, N, De Vijlder, T, Valkenborg, D, Laukens, K, Smets, K, Vreeken, J, Mertens, I, Carpentier, S, Panis, B, De Jaeger, G, Prinsen, E & Witters, E Unraveling Tobacco BY-2 Protein Complexes with BN PAGE/LC-MS/MS and Clustering Methods. Journal of Proteomics vol.74(8), pp 1201-1217, Elsevier, 2011. (IF 5.074)
Vreeken, J, van Leeuwen, M & Siebes, A Krimp: Mining Itemsets that Compress. Data Mining and Knowledge Discovery vol.23(1), pp 169-214, Springer, 2011. (IF 2.950)
2010
Mampaey, M & Vreeken, J Summarising Data by Clustering Items. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 321-336, Springer, 2010. (18% acceptance rate)implementation
Vreeken, J, Tatti, N & Goethals, B (eds) Proceedings of the ACM SIGKDD Workshop on Useful Patterns (UP). ACM Press, 2010.website
Vreeken, J, Tatti, N & Goethals, B Useful Patterns (UP'10) ACM SIGKDD Workshop Report. ACM SIGKDD Explorations vol.12(2), pp 56-58, ACM Press, 2010.
Vreeken, J Making Pattern Mining Useful. ACM SIGKDD Explorations vol.12(1), pp 75-76, ACM Press, 2010.
2009
van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 32-32, Springer, 2009. (ECMLPKDD'09 Best Student Paper)video
Heikinheimo, H, Vreeken, J, Siebes, A & Mannila, H Low-Entropy Set Selection. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 569-579, SIAM, 2009. (25% acceptance rate)
Vreeken, J Making Pattern Mining Useful. Dissertation, Universiteit Utrecht, , 2009.
van Leeuwen, M, Vreeken, J & Siebes, A Identifying the Components. Data Mining and Knowledge Discovery vol.19(2), pp 176-193, Springer, 2009. (IF 2.950) (ECMLPKDD'09 Special Issue) (Best Student Paper)video
2008
Tatti, N & Vreeken, J Finding Good Itemsets by Packing Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 588-597, IEEE, 2008. (9.8% acceptance rate)
Vreeken, J & Siebes, A Filling in the Blanks – Krimp Minimisation for Missing Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 1067-1072, IEEE, 2008. (19% acceptance rate)
2007
Vreeken, J, van Leeuwen, M & Siebes, A Preserving Privacy through Data Generation. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), pp 685-690, IEEE, 2007. (19% acceptance rate)
Siebes, A, van Leeuwen, M & Vreeken, J MDL for Pattern Mining. In: Proceedings of the International Conference on Statistics for Data Mining, Learning and Knowledge Extraction Models (IASC), 2007.
Vreeken, J, van Leeuwen, M & Siebes, A Characterising the Difference. In: Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp 765-774, ACM, 2007. (19% acceptance rate)
2006
van Leeuwen, M, Vreeken, J & Siebes, A Compression Picks the Item Sets that Matter. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp 585-592, Springer, 2006. (18% acceptance rate)
Siebes, A, Vreeken, J & van Leeuwen, M Item Sets That Compress. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 393-404, SIAM, 2006. (16% acceptance rate)
2004
Wiering, M, Vreeken, J, van Veenen, J & Koopman, ACM Simulation and Optimization of Traffic in a City. In: Proceedings of the IEEE Intelligent Vehicles Symposium (IV), pp 453-458, IEEE, 2004.
2003
Koopman, ACM, van Leeuwen, M & Vreeken, J Exploring Temporal Memory of LSTM and Spiking Circuits. In: Workshop on the Future of Neural Networks (FUNN), 2003.