Publications per year


2020

Dalleiger, S & Vreeken, J Explainable Data Decompositions. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'20), AAAI, 2020. (oral presentation; overall acceptance rate 20.6%)website
Zhang, Y, Humbert, M, Surma, B, Manoharan, P, Vreeken, J & Backes, M Towards Plausible Graph Anonymization. In: Proceedings of the Network and Distributed System Security Symposium (NDSS), The Internet Society, 2020.

2019

Marx, A & Vreeken, J Telling Cause from Effect by Local and Global Regression. Knowledge and Information Systems vol.60(3), pp 1277-1305, IEEE, 2019. (IF 2.397)website
Fischer, J & Vreeken, J Sets of Robust Rules, and How to Find Them. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Data (ECMLPKDD), Springer, 2019. (17.7% acceptance rate)website
Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), IEEE, 2019. (18.5% acceptance rate)website
Kaltenpoth, D & Vreeken, J We Are Not Your Real Parents: Telling Causal From Confounded by MDL. In: SIAM International Conference on Data Mining (SDM), SIAM, 2019. (22.9% acceptance rate)website
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Correlations in Categorical Data. In: Proceedings of the IEEE International Conference on Data Mining (ICDM'19), IEEE, 2019. (18.5% acceptance rate)website
Mandros, P, Boley, M & Vreeken, J Discovering Reliable Dependencies from Data: Hardness and Improved Algorithms (Extended Abstract). In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), IJCAI, 2019. (Invited contribution to the IJCAI Sister Conference Best Paper Track)website
Marx, A & Vreeken, J Identifiability of Cause and Effect using Regularized Regression. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'19), ACM, 2019. (oral presentation 9.2% acceptance rate; overall 14.2%)website
Marx, A & Vreeken, J Testing Conditional Independence on Discrete Data using Stochastic Complexity. In: Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR, 2019. (31% acceptance rate)website
Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the ECMLPKDD Workshop on Graph Embedding and Mining (GEM), 2019. (oral presentation, 21% acceptance rate)website
Kalofolias, J, Boley, M & Vreeken, J Discovering Robustly Connected Subgraphs with Simple Descriptions. In: Proceedings of the ACM SIGKDD Workshop on Mining and Learning from Graphs (MLG), 2019.website
Marx, A & Vreeken, J Approximating Algorithmic Conditional Independence for Discrete Data. In: Proceedings of the the First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI, AAAI, 2019.website
Saran, D & Vreeken, J Summarizing Dynamic Graphs using MDL. In: Proceedings of the ECMLPKDD Workshop on Graph Embedding and Mining (GEM), 2019. (oral presentation, 21% acceptance rate)
Cotop, SA How to be Grim: Explaining Data at Different Granularity Levels. M.Sc. Thesis, Saarland University, 2019.
Mian, OA Causal Discovery using MDL-based Regression. M.Sc. Thesis, Saarland University, 2019.
Saran, D Summarizing Dynamic Graphs using MDL. M.Sc. Thesis, Saarland University, 2019.

2018

Budhathoki, K & Vreeken, J Origo: Causal Inference by Compression. Knowledge and Information Systems vol.56(2), pp 285-307, Springer, 2018. (IF 2.247)website
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)
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)
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)website
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)website
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)website
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)website
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.
Wiegand, B Optimization of Work Roll Change Intervals through Data-Driven Roll-Wear Models at the Finishing Stand of a Four-High Rolling Mill. M.Sc. Thesis, Saarland University, 2018.
Farag, I Efficiently Summarising Data with Patterns that Overlap. M.Sc. Thesis, Saarland University, 2018.
Halbe, M OctOPUS: Branch-and-Bound Search with both Sibling Propagations and Closure Operators. M.Sc. Thesis, Saarland University, 2018.
Aburahma, M Smoothie: Smoothing Discrete Data. M.Sc. Thesis, Saarland University, 2018.
Eissfeller, M Reverse-Engineering Epidemics in Large Weighted Graphs. M.Sc. Thesis, Saarland University, 2018.
Dembelova, T More Robust Interaction Preserving Discretization. M.Sc. Thesis, Saarland University, 2018.
Brendel, Y Reconstructing Dependency Networks by Cumulate Entropy Estimation. M.Sc. Thesis, Saarland University, 2018.

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)
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%)website
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)website
Budhathoki, K & Vreeken, J Correlation by Compression. In: Proceedings of the SIAM Conference on Data Mining (SDM), SIAM, 2017. (25% acceptance rate)website
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%)website
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%)website
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)website
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)website
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.
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.
Hinrichs, F Finding Difference and Norm between Sequence Databases. B.Sc. Thesis, Saarland University, 2017.
Hättasch, B Automated Ontology Refinement using Compression-based Learning. M.Sc. Thesis, Technische Universität Darmstadt, 2017.
Jilke, H Explore: Discovering Power-Law Communities in Large Graphs. M.Sc. Thesis, Saarland University, 2017.
Burghartz, R Compress it with fire: adaptive codes for MDL-based pattern mining. M.Sc. Thesis, Saarland University, 2017.

2016

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)
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%)videowebsite
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)
Kalofolias, J, Galbrun, E & Miettinen, P From Sets of Good Redescriptions to Good Sets of Redescriptions. In: Proceedings of the IEEE International Conference on Data Mining (ICDM), IEEE, 2016. (full paper, 8.5% acceptance rate; overall 19.6%) (invited for the KAIS Special Issue on the Best of IEEE ICDM 2016)
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)implementation
website
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)implementation
website
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)implementation
website
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)website
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
Halbe, M Skim: Alternative Candidate Selections for Slim through Sketching. B.Sc. Thesis, Saarland University, 2016.
Baradaranshahroudi, A Fast Computation of Highest Correlated Segments in Multivariate Time-Series. M.Sc. Thesis, Saarland University, 2016.
Bhattacharyya, A Squish: Efficiently Summarising Sequences with Rich and Interleaving Patterns. M.Sc. Thesis, Saarland University, 2016.
Wójciak, BA Spaghetti: Finding Storylines in Large Collections of Documents. M.Sc. Thesis, Saarland University, 2016.
Grosse, K An Approach for Ontological Pattern-based Summarization. M.Sc. Thesis, Saarland University, 2016.
Gandhi, M Towards Summarising Large Transaction Databases. M.Sc. Thesis, Saarland University, 2016.
Salyaeva, M Summarising and Recommending with Skipisodes. M.Sc. Thesis, Saarland University, 2016.

2015

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.website
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)
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.
website
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.implementation
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.website
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
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.
Vreeken, J Causal Inference by Direction of Information. In: Proceedings of the SIAM International Conference on Data Mining (SDM), pp 909-917, SIAM, 2015.website
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
Budhathoki, K Correlation by Compression. M.Sc. Thesis, Saarland University, 2015.
Mandros, P Information-Theoretic Supervised Feature Selection for Continuous Data. M.Sc. Thesis, Saarland University, 2015.

2014

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)implementation
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)implementation
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)implementation
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
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)
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)
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)implementation
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)implementation
Vreeken, J & Tatti, N Interesting Patterns. In: Aggarwal, CC & Han, J (eds) Frequent Pattern Mining, pp 105-134, pp 105-134, 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.
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.
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
Bier, S Causal Inference by Packing Data. B.Sc. Thesis, Saarland University, 2014.

2013

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%)implementation
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
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.implementation
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
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.implementation
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