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

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

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

Günnemann, S., Färber, I., Müller, E., Assent, I. & Seidl, T. (2011). External Evaluation Measures for Subspace Clustering. In Proceedings of the 20th ACM international conference on Information and knowledge management (pp. 1363-1372). Association for Computing Machinery. https://doi.org/10.1145/2063576.2063774

Guo, C., Ma, M., Yang, B., Jensen, C. S. & Kaul, M. (2012). EcoMark: Evaluating Models of Vehicular Environmental Impact. In I. Cruz & C. Knoblock (Eds.), Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL GIS 2012 (pp. 269-278). Association for Computing Machinery. https://doi.org/10.1145/2424321.2424356

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

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

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

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

Dang, X-H., Assent, I., Ng, R. T., Zimek, A. & Schubert, E. (2014). Discriminative features for identifying and interpreting outliers. In Proceedings, 2014 IEEE 30th International Conference on Data Engineering (ICDE) (pp. 88-99). [6816642] IEEE Computer Society Press. https://doi.org/10.1109/ICDE.2014.6816642

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