Publications

Zimek, A., Assent, I. & Vreeken , J. (2014). Frequent Pattern Mining Algorithms for Data Clustering. In C. C. Aggarwal & J. Han (Eds.), Frequent Pattern Mining (pp. 403-423). Springer. https://doi.org/10.1007/978-3-319-07821-2_16

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

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

Wichterich, M., Kranen, P., Assent, I. & Seidl, T. (2010). Efficient EMD-based Similarity Search in Medical Image Databases. In C. Plant & C. Böhm (Eds.), Database Technology for Life Sciences and Medicine (pp. 175-202). World Scientific. Science, Engineering and Biology Informatics Vol. 6

Wichterich, M., Assent, I., Philipp, K. & Seidl, T. (2008). Efficient EMD-based Similarity Search in Multimedia Databases via Flexible Dimensionality Reduction. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data (pp. 199-212) https://doi.org/10.1145/1376616.1376639

Vicente, C. R., Assent, I. & Jensen, C. S. (2011). Effective Privacy-Preserving Online Route Planning. In 2011 12th IEEE International Conference on Mobile Data Management (MDM) (pp. 119-128). IEEE Computer Society Press. https://doi.org/10.1109/MDM.2011.20

Thai Son, M., Assent, I. & Storgaard, M. (2016). AnyDBC: An efficient anytime density-based clustering algorithm for very large complex datasets. In KDD 2016 - Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1025-1034). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939750

Thai Son, M., Assent, I. & Le, A. T. (2016). Anytime OPTICS: An efficient approach for hierarchical density-based clustering. In S. B. Navathe, W. Wu, S. Shekhar, X. Du, X. Sean Wang & H. Xiong (Eds.), Database Systems for Advanced Applications - 21st International Conference, DASFAA 2016, Proceedings (Vol. 9642, pp. 164-179). Springer VS. Lecture Notes in Computer Science (LNCS) Vol. 9642 https://doi.org/10.1007/978-3-319-32025-0_11

Thai Son, M., Assent, I., Birk, M. S., Dieu, M. S., Jacobsen, J., Kristensen, J., Rahman, H., Thirumuruganathan, S., Das, G., Omidvar-Tehrani, B., Borromeo, R. M., Chen, L., Miller, R., Benouaret, I., Amer-Yahia, S. & Roy, S. B. (2019). An Efficient Greedy Algorithm for Sequence Recommendation. In DEXA 2019: International Conference on Database and Expert Systems Applications, Aug 2019, Linz, Austria (pp. 314-326) https://doi.org/10.1007/978-3-030-27615-7_24

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

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). Finding Top-k Relevant Groups of Spatial Web Objects. V L D B Journal, 24(4), 537-555. https://doi.org/10.1007/s00778-015-0388-z

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

Skovsgaard, A., Sidlauskas, D. & Jensen, C. S. (2014). A Clustering Approach to the Discovery of Points of Interest from Geo-Tagged Microblog Posts. In J. E. Guerrero (Ed.), 15th IEEE International Conference on Mobile Data Management (MDM), 2014 : Proceedings (pp. 178-189 (vol. 1)). IEEE Computer Society Press. https://doi.org/10.1109/MDM.2014.28

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

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/

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

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

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

Nguyen, H. V., Gopalkrishnan, V. & Assent, I. (2011). An Unbiased Distance-based Outlier Detection Approach for High-dimensional Data. Lecture Notes in Computer Science, 6587, 138-152. https://doi.org/10.1007/978-3-642-20149-3_12

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

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

Neerbek, J., Assent, I. & Dolog, P. (2018). Detecting Complex Sensitive Information via Phrase Structure in Recursive Neural Networks. In D. Phung, V. S. Tseng, G. I. Webb, B. Ho, M. Ganji & L. Rashidi (Eds.), Advances in Knowledge Discovery and Data Mining: PAKDD '18 (Vol. 10939, pp. 373-385). Springer VS. Lecture Notes in Computer Science (LNCS) No. 10939 https://doi.org/10.1007/978-3-319-93040-4_30

Neerbek, J., Eskildsen, M., Dolog, P. & Assent, I. (2020). A real-world data resource of complex sensitive sentences based on documents from the Monsanto trial. In N. Calzolari, F. Bechet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk & S. Piperidis (Eds.), LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings (pp. 1258-1267). European Language Resources Association.

Müller, E., Assent, I., Günnemann, S., Gerwert, P., Hannen, M., Jansen, T. & Seidl, T. (2011). A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. In T. Härder, W. Lehner, B. Mitschang, H. Schöning & H. Schwarz (Eds.), Proceedings of the 14th GI Conference on Database Systems for Business, Technology, and the Web (BTW 2011) (pp. 347-366). Gesellschaft für Informatik e.V..

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., Krieger, R., Günnemann, S. & Seidl, T. (2009). DensEst: Density Estimation for Data Mining in High Dimensional Spaces. In Proceedings of the Ninth SIAM International Conference on Data Mining (pp. 175-186) http://www.siam.org/proceedings/datamining/2009/dm09.php

Müller, E., Günnemann, S., Assent, I. & Seidl, T. (2009). Evaluating Clustering in Subspace Projections of High Dimensional Data. V L D B Journal, (1), 1270-1281. http://www.informatik.uni-trier.de/~ley/db/journals/pvldb/pvldb2.html

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

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

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/

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. & 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

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

Mortensen, M. L., Chester, S., Assent, I. & Magnani, M. (2015). Efficient caching for constrained skyline queries. In International Conference on Extending Database Technology (EDBT 2015) (pp. 337-348). openproceedings.org. https://doi.org/10.5441/002/edbt.2015.30

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

Micenková, B., Ng, R. T., Dang, X-H. & Assent, I. (2013). Explaining outliers by subspace separability. In H. Xiong, G. Karypis, B. Thuraisingham, D. Cook & X. Wu (Eds.), Proceedings, IEEE 13th International Conference on Data Mining (ICDM 2013) (pp. 518 - 527 ). IEEE Press. IEEE International Conference on Data Mining (ICDM'13) http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6724379

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

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

Mai, S. T., Assent, I., Jacobsen, J. & Dieu, M. S. (2018). Anytime parallel density-based clustering. Data Mining and Knowledge Discovery, 32(4), 1121-1176. https://doi.org/10.1007/s10618-018-0562-1

Magnani, M., Assent, I. & Mortensen, M. L. (2012). Anytime skyline query processing for interactive systems. Paper presented at International Workshop on Ranking in Databases, Istanbul, Turkey. http://chenwsdb.fulton.ad.asu.edu/DBRank2012/

Magnani, M. & Assent, I. (2013). From stars to galaxies: skyline queries on aggregate data. In G. Guerrini & N. W. . Paton (Eds.), International Conference on Extending Database Technology (pp. 477-488 ). Association for Computing Machinery. https://doi.org/10.1145/2452376.2452432

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

Ma, M., Yang, B. & Jensen, C. S. (2014). Enabling Time-Dependent Uncertain Eco-Weights For Road Networks. In M. A. Nascimento & M. Renz (Eds.), Proceedings of the 1st International ACM Workshop on Managing and Mining Enriched Geo-spatial Data: GeoRich 2014 [1] Association for Computing Machinery. https://doi.org/10.1145/2619112.2619113

Ma, M. Y. (2015). Enabling Time-Dependent Uncertain Edge Weights and Stochastic Routing in Road Networks. Department of Computer Science, Aarhus University.

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

Kremer, H., Günnemann, S., Ivanescu, A-M., Assent, I. & Seidl, T. (2011). Efficient Processing of Multiple DTW Queries in Time Series Databases. Lecture Notes in Computer Science, 6809, 150-167. https://doi.org/10.1007/978-3-642-22351-8_9

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

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

Kranen, P., Assent, I. & Seidl, T. (2012). An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams. Datenbank-Spektrum, 12(1), 43-50 . https://doi.org/10.1007/s13222-012-0083-9

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