Publications

Bøgh, K. S., Chester, S. & Assent, I. (2015). Work-Efficient Parallel Skyline Computation for the GPU. Proceedings of the VLDB Endowment, 8(9), 962-973. https://doi.org/10.14778/2777598.2777605

Enni, S. & Assent, I. (2018). Using Balancing Terms to Avoid Discrimination in Classification. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 947-952). IEEE Press. https://doi.org/10.1109/ICDM.2018.00116

Yang, B., Guo, C., Ma, Y. & Jensen, C. S. (2015). Toward personalized, context-aware routing. V L D B Journal, 24(2), 297-318. https://doi.org/10.1007/s00778-015-0378-1

Skovsgaard, A. & Jensen, C. S. (2014). Top-k point of interest retrieval using standard indexes. In Y. Huang & M. Schneider (Eds.), Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL '14 (pp. 173-182 ). Association for Computing Machinery. https://doi.org/10.1145/2666310.2666399

Assent, I., Krieger, R., Afschari, F. & Seidl, T. (2008). The TS-Tree: Efficient Time Series Search and Retrieval. In Proceedings of the 11th international conference on Extending database technology: Advances in database technology (Vol. 261, pp. 252-263) https://doi.org/10.1145/1353343.1353376

Kranen, P., Assent, I., Baldauf, C. & Seidl, T. (2011). The ClusTree : indexing micro-clusters for anytime stream mining. Knowledge and Information Systems, 29(2), 249-272. https://doi.org/10.1007/s10115-010-0342-8

Bøgh, K. S., Chester, S., Sidlauskas, D. & Assent, I. (2017). Template Skycube Algorithms for Heterogeneous Parallelism on Multicore and GPU Architectures. In Proceedings of the 2017 International Conference on Management of Data, (ACM SIGMOD) (pp. 447-462). Association for Computing Machinery. https://doi.org/10.1145/3035918.3035962

Magnani, M., Assent, I. & Mortensen, M. L. (2014). Taking the Big Picture: representative skylines based on significance and diversity. V L D B Journal, 23(5), 795-815. https://doi.org/10.1007/s00778-014-0352-3

Neerbek, J., Assent, I. & Dolog, P. (2017). TABOO: Detecting unstructured sensitive information using recursive neural networks. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 1399-1400). [7930091] IEEE Computer Society Press. Proceedings of the International Conference on Data Engineering Vol. 33 https://doi.org/10.1109/ICDE.2017.195

Shao, J., Tan, Y., Gao, L., Yang, Q., Plant, C. & Assent, I. (2019). Synchronization-based clustering on evolving data stream. Information Sciences, 501, 573-587. https://doi.org/10.1016/j.ins.2018.09.035

Cao, X., Chen, L., Cong, G., Jensen, C. S., Qu, Q., Skovsgaard, A., Wu, D. & Yiu, M. L. (2012). Spatial Keyword Querying: Invited Paper. Lecture Notes in Computer Science, 7532, 16-29 . https://doi.org/10.1007/978-3-642-34002-4_2

Magnani, M., Assent, I., Hornbæk, K., Jakobsen, M. R. & Larsen, K. F. (2013). SkyView: a user evaluation of the skyline operator. In Q. He & A. Iyengar (Eds.), Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM '13 (pp. 2249-2254 ). Association for Computing Machinery. https://doi.org/10.1145/2505515.2505739

Bøgh, K. S., Chester, S. & Assent, I. (2016). SkyAlign: a portable, work-efficient skyline algorithm for multicore and GPU architectures. The VLDB Journal, 25(6), 817-841. https://doi.org/10.1007/s00778-016-0438-1

Neerbek, J., Dolog, P. & Assent, I. (2019). Selective Training: A Strategy for Fast Backpropagation on Sentence Embeddings. In Q. Yang, M-L. Zhang, Z. Gong, S-J. Huang & Z-H. Zhou (Eds.), Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Proceedings: PAKDD '19 (pp. 40-53). Springer. https://doi.org/10.1007/978-3-030-16142-2_4

Mai, S. T., Dieu, M. S., Assent, I., Jacobsen, J., Kristensen, J. & Birk, M. (2017). Scalable and interactive graph clustering algorithm on multicore CPUs. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (pp. 349-360). IEEE Computer Society Press. Proceedings of the International Conference on Data Engineering https://doi.org/10.1109/ICDE.2017.94

Skovsgaard, A., Sidlauskas, D. & Jensen, C. S. (2014). Scalable Top-k Spatio-Temporal Term Querying. ICDE, 148-159. https://doi.org/10.1109/ICDE.2014.6816647

Skovsgaard, A. & Jensen, C. S. (2015). Scalable Spatio-Textual Region Querying in Key-Value Stores. Paper presented at ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Dallas, United States. http://sigspatial2014.sigspatial.org/

Chester, S., Sidlauskas, D., Assent, I. & Bøgh, K. S. (2015). Scalable Parallelization of Skyline Computation for Multi-core Processors. In 31st IEEE International Conference on Data Engineering (ICDE 2015) (pp. 1083 - 1094). IEEE Computer Society Press. https://doi.org/10.1109/ICDE.2015.7113358

Son, M. T., Amer-Yahia, S., Assent, I., Birk, M., Storgaard Dieu, M., Jacobsen, J. & Kristensen, J. (2019). Scalable Interactive Dynamic Graph Clustering on Multicore CPUs. IEEE Transactions on Knowledge and Data Engineering, 31(7), 1239-1252. [8340880]. https://doi.org/10.1109/TKDE.2018.2828086

Müller, E., Assent, I., Günnemann, S. & Seidl, T. (2011). Scalable Density-Based Subspace Clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1077-1086). Association for Computing Machinery. https://doi.org/10.1145/2063576.2063733

Müller, E., Assent, I., Günnemann, S., Krieger, R. & Seidl, T. (2009). Relevant Subspace Clustering: Mining the Most Interesting Non-Redundant Concepts in High-Dimensional Data. In Proc. IEEE International Conference on Data Mining (ICDM 2009) (pp. 377-386) https://doi.org/10.1109/ICDM.2009.10

Ciosici, M. R., Derczynski, L. & Assent, I. (2019). Quantifying the morphosyntactic content of Brown Clusters. In J. Burstein, C. Doran & T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Vol. 1, pp. 1541-1550). Association for Computational Linguistics. https://aclweb.org/anthology/papers/N/N19/N19-1157/

Bouros, P., Assent, I., Jeppesen, J. H. & Toftegaard, T. S. (2019). Processing satellite imagery for decision-making in precision agriculture. In P. Papotti (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference (EDBT/ICDT 2019) Sun Site Central Europe. https://bigvis.imsi.athenarc.gr/bigvis2019/papers/BigVis_2019_paper_4.pdf

Assent, I., Domeniconi, C., Gullo, F., Tagarelli, A. & Zimek, A. (Eds.) (2013). Proceedings of the 4th MultiClust Workshop on Multiple Clusterings, Multi-view Data, and Multi-source Knowledge-driven Clustering. Association for Computing Machinery. http://dl.acm.org.ez.statsbiblioteket.dk:2048/citation.cfm?id=2501006&coll=DL&dl=ACM

Assent, I., Kremer, H., Günnemann, S. & Seidl, T. (2010). Pattern Detector: Fast Detection of Suspicious Stream Patterns for Immediate Reaction. In Proc. International Conference on Extending Database Technology (EDBT/ICDT 2010), Lausanne, Switzerland http://www.edbt.org/Proceedings/2010-Lausanne/

Yiu, M. L., Assent, I. & Jensen, C. S. (2012). Outsourced Similarity Search on Metric Data Assets. I E E E Transactions on Knowledge & Data Engineering, 24(2), 338-352. https://doi.org/10.1109/TKDE.2010.222

Seidl, T., Müller, E., Assent, I. & Steinhausen, U. (2008). Outlier detection and ranking based on subspace clustering. Dagstuhl Seminar Proceedings. http://www.dagstuhl.de/Materials/index.en.phtml?08421

Muller, E., Assent, I., Iglesias, P., Mülle, Y. & Böhm, K. B. (2012). Outlier Ranking via Subspace Analysis in Multiple Views of the Data. In Proceedings of the 12th IEEE International Conference on Data Mining, ICDM (pp. 529 - 538 ). IEEE Communications Society. https://doi.org/10.1109/ICDM.2012.112

Dang, X-H., Micenková, B., Assent, I. & Ng, R. T. (2013). Outlier Detection with Space Transformation and Spectral Analysis. In C. Kamath, J. Dy, Z. Obradovic, J. Ghosh, S. Parthasarathy & Z-H. Zhou (Eds.), Proceedings of the 2013 SIAM International Conference on Data Mining, SDM (pp. 225-233). Society for Industrial and Applied Mathematics. https://doi.org/10.1137/1.9781611972832.25

Müller, E., Assent, I., Steinhausen, U. & Seidl, T. (2008). OutRank: ranking outliers in high dimensional data. In IEEE 24th International Conference on Data Engineering Workshop, 2008. ICDEW 2008 (pp. 600-603) https://doi.org/10.1109/ICDEW.2008.4498387

Müller, E., Assent, I., Günnemann, S. & Seidl, T. (2009). OpenSubspace: An Open Source Framework for Evaluation and Exploration of Subspace Clustering Algorithms in WEKA. In Proceedings of OSDM 2009 (pp. 1-12) http://osdm09.togaware.com/

Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., Assent, I. & Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30(4), 891-927. https://doi.org/10.1007/s10618-015-0444-8

Chester, S., Mortensen, M. L. & Assent, I. (2014). On the Suitability of Skyline Queries for Data Exploration. In K. S. Candan et al. (Ed.), Proceedings of the Workshops of the EDBT/ICDT 2014 Joint Conference: 1st International Workshop on Exploratory Search in Databases and the Web (ExploreDB 2014) (pp. 161-166). CEUR-WS.org. CEUR Workshop Proceedings http://ceur-ws.org/Vol-1133/paper-27.pdf

Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenkova, B., Schubert, E., Assent, I. & Houle, M. E. (2016). On the Evaluation of Outlier Detection: Measures, Datasets, and an Empirical Study Continued. In R. Krestel, D. Mottin & E. Müller (Eds.), Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (Vol. 1670, pp. 1). ceur workshop proceedings. CEUR Workshop Proceedings Vol. 1670 http://ceur-ws.org/Vol-1670/paper-55.pdf

Dang, X-H., Assent, I. & Bailey, J. (2012). Multiple Clustering Views via Constrained Projections. In E. Müller, T. Seidl & A. Zimek (Eds.), 2012 SIAM International Conference on Data Mining: 3rd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings (pp. 23-30). Society for Industrial and Applied Mathematics. http://siam.omnibooksonline.com/2012datamining/data/papers/WS06.pdf

Müller, E., Assent, I., Günnemann, S., Seidl, T. & Dy, J. (2015). MultiClust special issue on discovering, summarizing and using multiple clusterings. Machine Learning, 98(1-2), 1-5. https://doi.org/10.1007/s10994-014-5445-0

Mortensen, M. L. (2016). Multi-Criteria Decision Support Queries in Exploratory & Open World Settings.

Müller, E., Assent, I., Krieger, R., Jansen, T. & Seidl, T. (2008). Morpheus: Interactive Exploration of Subspace Clustering. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1089-1092) https://doi.org/10.1145/1401890.1402026

Assent, I. (2010). Mining and representing recommendations in actively evolving recommender systems. In International Workshop on Modeling, Managing and Mining of Evolving Social Networks (M3SN), in conjunction with IEEE International Conference on Data Engineering (ICDE 2010), Long Beach, CA, USA (pp. 282-285) https://doi.org/10.1109/ICDEW.2010.5452714

Dang, X-H., Micenková, B., Assent, I. & Ng, R. T. (2013). Local Outlier Detection with Interpretation. In H. Blockeel, K. Kersting , S. Nijssen & F. Železný (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III (pp. 304-320 ). Springer VS. Lecture Notes in Computer Science Vol. 8190 https://doi.org/10.1007/978-3-642-40994-3_20

Assent, I., Müller, E., Günnemann, S., Krieger, R. & Seidl, T. (2010). Less is More: Non-Redundant Subspace Clustering. In 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings (MultiClust 2010) in conjunction with 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA (2010)

Micenková, B., McWilliams, B. & Assent, I. (2014). Learning Outlier Ensembles: The Best of Both Worlds–Supervised and Unsupervised. Paper presented at ACM SIGKDD 2014 Workshop ODD2 , New York, United States. http://outlier-analytics.org/odd14kdd/odd-2014-proceedings.pdf

Assent, I., Domeniconi, C., Gionis, A. & Hüllermeier, E. (2020). Introduction to the special issue of the ECML PKDD 2020 journal track. Data Mining and Knowledge Discovery, 34(5), 1235-1236. https://doi.org/10.1007/s10618-020-00713-9

Kristensen, J., Mai, S. T., Assent, I., Jacobsen, J., Vo, B. & Le, A. (2017). Interactive exploration of subspace clusters for high dimensional data. In D. Benslimane, E. Damiani, W. I. Grosky, A. Hameurlain, A. Sheth & R. R. Wagner (Eds.), Database and Expert Systems Applications - 28th International Conference, DEXA 2017, Proceedings (Vol. 10438 LNCS, pp. 327-342). Springer VS. Lecture Notes in Computer Science Vol. 10438 https://doi.org/10.1007/978-3-319-64468-4_25

Pham, T. H., Kristensen, J., Mai, S. T., Assent, I., Jacobsen, J., Vo, B. & Le, A. (2018). Interactive Exploration of Subspace Clusters on Multicore Processors. In A. Hameurlain, R. Wagner, D. Benslimane, E. Damiani & W. I. Grosky (Eds.), Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX: Special Issue on Database- and Expert-Systems Applications (Vol. 11310, pp. 169-199). Springer VS. Lecture Notes in Computer Science (LNCS) Vol. 11310 https://doi.org/10.1007/978-3-662-58415-6_6

Skovsgaard, A. (2014). Indexing, Query Processing, and Clustering of Spatio-Temporal Text Objects. Department of Computer Science, Aarhus University.

Kranen, P., Müller, E., Assent, I., Krieger, R. & Seidl, T. (2010). Incremental Learning of Medical Data for Multi-Step Patient Health Classification. In C. Plant & C. Böhm (Eds.), Database Technology for Life Sciences and Medicine (pp. 321-344). World Scientific. Science, Engineering and Biology Informatics Vol. 6

Assent, I., Krieger, R., Müller, E., ller, E. & Seidl, T. (2008). INSCY: indexing subspace clusters with in-process-removal of redundancy. In Eighth IEEE International Conference on Data Mining, 2008. ICDM '08. (pp. 719-724) https://doi.org/10.1109/ICDM.2008.46

Assent, I., Günnemann, S., Kremer, H. & Seidl, T. (2009). High-dimensional Indexing for Multimedia Features. Lecture Notes in Informatics, 187-206. http://btw2009.uni-muenster.de/oe20/cms/website.php?id=/de/index/programm.htm

Bøgh, K. S., Chester, S., Sidlauskas, D. & Assent, I. (2014). Hashcube: A Data Structure for Space- and Query-Efficient Skycube Compression. In J. Li & X. S. Wang (Eds.), Proceedings of The 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014) (pp. 1767-1770). Association for Computing Machinery. https://doi.org/10.1145/2661829.2661891

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