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Research
Research Interests
My research interests are broadly knowledge discovery in databases and machine learning.
More particular interests are currently ensemble techniques for unsupervised learning
(clustering, outlier detection), and applications and problems in high dimensional data.
Development of new models is challenging in various aspects.
A major problem (and a topic I am especially interested in)
is however the evaluation of unsupervised methods and the interpretation and reliability of their results,
in the development of methods as well as in application scenarios.
At large, this is related to the question of how to do data mining research in a scientifically well-founded way.
Professional Services
Invited Talks
| [33] | Fairness in Imbalanced Classification: An Adjustment to the k Nearest Neighbor Classifier.
CODE-colloquium, University of the Bundeswehr Munich, Germany, 22.11.2023.
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| [32] | Clustering High-dimensional Data.
Symposium ''Clustering, graphs and hierarchies -- finding order in (un)structured data'', RWTH Aachen, Germany, 11.08.2023.
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| [31] | Basic Data Mining Approaches to Anomaly Detection and Challenges in High-Dimensional Data.
Session ''Anomaly Detection for Complex Data'' at Data Science, Statistics and Visualization (DSSV), Rotterdam, The Netherlands (online), 08.07.2021.
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| [30] | Dimensionality and Scalability Issues in Outlier Detection.
Round table discussion (panel) ''Dimensionality and Scalability Issues in Web search and Data Mining'' at the 14th ACM Int. Conf. Web Search and Data Mining (WSDM), Jerusalem, Israel (online), 09.03.2021.
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| [29] | Good and Bad Neighborhood Approximations for Outlier Detection Ensembles.
NII Seminar Series on Dimensionality and Scalability VII, National Institute of Informatics, Tokyo, Japan, 13.03.2018.
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| [28] | Data Mining in High Dimensional Data.
DIRECT workshop, Aarhus University, Denmark, 12.01.2018.
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| [27] | On the Evaluation of Unsupervised Outlier Detection.
Technical University of Vienna, Austria, 18.05.2016.
[ slides (pdf) ] |
| [26] | Get Better Results Faster on Bigger Data: How Approximations can Improve Data Analysis Results.
University of Luxembourg, 19.04.2016.
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| [25] | Outlier Detection with Approximations and Ensembles.
Aarhus University, Denmark, 14.04.2016.
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| [24] | Outlier Detection with Approximations and Ensembles.
TU Darmstadt, Germany, 15.03.2016.
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| [23] | Ensembles for Unsupervised Outlier Detection: Challenges and Solutions.
Concordia University, Montreal, QC, Canada, 09.03.2016.
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| [22] | Ensembles for Unsupervised Outlier Detection: Challenges and Solutions.
University of Southern Denmark, Odense, Denmark, 03.03.2016.
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| [21] | On the Evaluation of Unsupervised Outlier Detection.
National Institute of Informatics, Tokyo, Japan, 25.02.2016.
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| [20] | Generalized Local Outlier Detection.
SnT - Interdisciplinary Centre for Security, Reliability and Trust, Luxembourg, 21.12.2015.
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| [19] | Ensembles for Unsupervised Outlier Detection: Challenges and Solutions.
ICMC, USP, São Carlos, Brazil, 25.11.2015.
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| [18] | Density-based Clustering.
University of Gothenburg, Sweden, 20.10.2015.
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| [17] | The Blind Men and the Elephant.
Workshop on Clustering in Big Data, Istituto Italiano Studi Filosofici, Palazzo Serra di Cassano, Naples, Italy, 29.05.2015.
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| [16] | The Blind Men and the Elephant.
National Institute of Informatics, Tokyo, Japan, 23.03.2015.
[ slides (pdf) ] |
| [15] | Ensemble Learning for Outlier Detection: Challenges and Solutions.
Carleton University, Ottawa, ON, Canada, 13.02.2015.
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| [14] | Challenges for Unsupervised Ensemble Learning.
Workshop: Similarity, k-NN, Dimensionality, Multimedia Databases, IRISA-INRIA, Rennes, France, 21.11.2014.
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| [13] | There and Back Again: Outlier Detection between Statistical Reasoning and Efficient Database Methods.
ICMC, USP, São Carlos, Brazil, 10.09.2014.
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| [12] | There and Back Again: Outlier Detection between Statistical Reasoning and Efficient Database Methods.
Technical University of Vienna, Austria, 15.05.2014.
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| [11] | Mind the Gap! Research in Data Mining as a Bridge Between Different Areas.
University of Vienna, Austria, 05.05.2014.
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| [10] | Challenges for Outlier Ensembles and a Subsampling-based Ensemble.
National Institute of Informatics, Tokyo, Japan, 25.03.2014.
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| [9] | Ensembles for Outlier Detection.
Aarhus University, Denmark, 15.01.2014.
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| [8] | Ensembles for Outlier Detection.
University of Copenhagen, Denmark, 07.10.2013.
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| [7] | There and Back Again: Outlier Detection between Statistical Reasoning and Efficient Database Methods.
Database Seminar Series (2012-2013) at the David R. Cheriton School of Computer Science, University of Waterloo, ON, Canada, 28.11.2012.
[ slides (pdf) ] |
| [6] | There and Back Again: Outlier Detection between Statistical Reasoning and Efficient Database Methods.
Simon Fraser University, Vancouver, BC, Canada, 16.11.2012.
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| [5] | Outlier Detection.
guest lecture in the course CMPUT 697 on Spatial Data Management and Data Mining, University of Alberta, Edmonton, AB, Canada, 28.9.+1.10.2012
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| [4] | Data Mining and the "Curse of Dimensionality".
3rd International Workshop with Mentors on Databases, Web and Information Management for Young Researchers (iDB Workshop 2011), Kyoto, Japan, 2.8.2011.
[ slides (pdf) | iDB Workshop 2011 (webpage) ] |
| [3] | Clustering Methods in High-Dimensional Spaces.
Roche Diagnostics GmbH, Penzberg, Germany, 22.7.2011.
[ slides (pdf) ] |
| [2] | Clustering in Subspaces of High-Dimensional Data.
National Institute of Informatics, Tokyo, Japan, 6.4.2010.
[ slides (pdf) ] |
| [1] | Clustering in Subspaces of High-Dimensional Data.
RWTH Aachen, Germany, 2.3.2010.
[ slides (pdf) ] |
Tutorials
| [11] | Youcef Djenouri, Arthur Zimek:
Outlier Detection in Urban Traffic Data.
Proceedings of the 8th International Conference on Web Intelligence, Mining and Semantics (WIMS), Novi Sad, Serbia, pp. 3:1-3:12, 2018.
[ EE (ACM) ] |
| [10] | Arthur Zimek, Erich Schubert, Hans-Peter Kriegel:
Outlier Detection in High-Dimensional Data.
Tutorial at the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Gold Coast, Australia, 2013.
[ tutorial slides | tutorial webpage ] |
| [9] | Arthur Zimek, Erich Schubert, Hans-Peter Kriegel:
Outlier Detection in High-Dimensional Data.
Tutorial at the 12th IEEE International Conference on Data Mining (ICDM), Brussels, Belgium, 2012.
[ tutorial slides | tutorial webpage ] |
| [8] | Hans-Peter Kriegel, Irene Ntoutsi, Myra Spiliopoulou, Grigorios Tsoumakas, Arthur Zimek:
Mining Complex Dynamic Data.
Tutorial at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Athens, Greece, 2011.
[ abstract | webpage ] |
| [7] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Outlier Detection Techniques.
Tutorial at 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC, 2010.
[ abstract | slides (pdf) ] |
| [6] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Outlier Detection Techniques.
Tutorial at 10th SIAM International Conference on Data Mining (SDM 2010), Columbus, OH, 2010.
[ abstract | slides (pdf) ] |
| [5] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Outlier Detection Techniques.
Tutorial at the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009), Bangkok, Thailand, 2009.
[ slides (pdf) ] |
| [4] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Detecting Clusters in Moderate-to-high Dimensional Data: Subspace Clustering, Pattern-based Clustering, Correlation Clustering.
Tutorial at the 34th International Conference on Very Large Databases (VLDB 2008), Auckland, New Zealand, 2008.
[ abstract (pdf) | EE (VLDB endowment) | slides (pdf) ] |
| [3] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Detecting Clusters in Moderate-to-high Dimensional Data: Subspace Clustering, Pattern-based Clustering, Correlation Clustering.
Tutorial at the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008), Las Vegas, NV, 2008.
[ slides (pdf) ] |
| [2] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Detecting Clusters in Moderate-to-high Dimensional Data: Subspace Clustering, Pattern-based Clustering, Correlation Clustering.
Tutorial at the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2008), Osaka, Japan, 2008.
[ abstract | slides (pdf) ] |
| [1] | Hans-Peter Kriegel, Peer Kröger, Arthur Zimek:
Detecting Clusters in Moderate-to-high Dimensional Data: Subspace Clustering, Pattern-based Clustering, Correlation Clustering.
Tutorial at the 7th International Conference on Data Mining (ICDM 2007), Omaha, NE, 2007.
[ abstract | slides (pdf) ] |
Editorial Board Member/Associate Editor/Action Editor
Guest Editor (Special Issues)
Reviewer for Journals (selection)
Member of Program Committees (selection)
- AAAI - AAAI Conference on Artificial Intelligence: 2017, 2018
- CIKM - ACM International Conference on Information and Knowledge Management: 2013 (senior), 2014, 2015
- ECML PKDD - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases: 2012, 2013, 2014, 2015, 2016, 2017, 2019, 2020-2022 (guest editorial board member for journal track)
- ICML - International Conference on Machine Learning: 2017, 2018
- MMM - International Conference on Multimedia Modeling, 2017, 2018
- PAKDD - Pacific Asia Conference on Knowledge Discovery and Data Mining: 2016, 2017, 2018, 2019
- SDM - SIAM International Conference on Data Mining: 2015, 2016, 2017, 2018 (senior), 2020, 2021, 2022, 2023, 2024
- SIGKDD ACM SIGKDD International Conference on Knowledge Discovery and Data Mining: 2011, 2012, 2013, 2014, 2015, 2016, 2021 (senior)
- SISAP - International Conference on Similarity Search and Applications: 2016, 2017, 2018, 2019, 2021, 2022, 2023
- SSDBM - International Conference on Scientific and Statistical Database Management: 2018
Workshop and Conference Organization
Other Services
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