![[höher]](/style/images/multi_up.png)
Eine Ebene höherGruppiert nach:
Dokumententyp
| Veröffentlichungsdatum
Anzahl der Publikationen: 35
Zeitschriftenartikel
Hohma, Ellen; Frey, Christian M. M.; Beer, Anna und Seidl, Thomas
(2022):
SCAR.
In: Proceedings of the VLDB Endowment, Bd. 15, Nr. 11: S. 3031-3044
Ullmann, Theresa 
ORCID: https://orcid.org/0000-0003-1215-8561; Beer, Anna ORCID: https://orcid.org/0000-0002-6890-997X; Hünemörder, Maximilian ORCID: https://orcid.org/0000-0001-9848-3714; Seidl, Thomas und Boulesteix, Anne-Laure ORCID: https://orcid.org/0000-0002-2729-0947
(2022):
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study.
In: Advances in Data Analysis and Classification, Bd. 17, Nr. 1: S. 211-238
[PDF, 856kB]
ORCID: https://orcid.org/0000-0003-1215-8561; Beer, Anna ORCID: https://orcid.org/0000-0002-6890-997X; Hünemörder, Maximilian ORCID: https://orcid.org/0000-0001-9848-3714; Seidl, Thomas und Boulesteix, Anne-Laure ORCID: https://orcid.org/0000-0002-2729-0947
(2022):
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study.
In: Advances in Data Analysis and Classification, Bd. 17, Nr. 1: S. 211-238
[PDF, 856kB]
Lu, Yifeng; Zhang, Yao; Richter, Florian und Seidl, Thomas
(2020):
k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity.
In: 2020 International Joint Conference on Neural Networks (Ijcnn)
Richter, Florian; Lu, Yifeng; Zellner, Ludwig; Sontheim, Janina und Seidl, Thomas
(2020):
TOAD: Trace Ordering for Anomaly Detection.
In: 2020 2nd International Conference on Process Mining (ICPM 2020): S. 169-176
Kazempour, Daniyal; Beer, Anna; Kroger, Peer und Seidl, Thomas
(2020):
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering.
In: 20th IEEE International Conference on Data Mining Workshops (ICDMW 2020): S. 316-323
Kazempour, Daniyal; Kroeger, Long Matthias Yan Peer und Seidl, Thomas
(2020):
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters.
In: 20th IEEE International Conference on Data Mining Workshops (ICDMW 2020): S. 308-315
Kazempour, Daniyal; Kroeger, Peer und Seidl, Thomas
(2020):
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering.
In: 20th IEEE International Conference on Data Mining Workshops (ICDMW 2020): S. 300-307
Beer, Anna; Seeholzer, Dominik; Schueler, Nadine-Sarah und Seidl, Thomas
(2020):
Angle-Based Clustering.
In: Similarity Search and Applications, Sisap 2020, Bd. 12440: S. 312-320
Richter, Florian; Lu, Yifeng; Kazempour, Daniyal und Seidl, Thomas
(2020):
Show Me the Crowds! Revealing Cluster Structures Through AMTICS.
In: Data Science and Engineering, Bd. 5, Nr. 4: S. 360-374
Trautmann, Dietrich; Fromm, Michael; Tresp, Volker; Seidl, Thomas und Schütze, Hinrich
(2020):
Relational and Fine-Grained Argument Mining. The LMU Munich project ReMLAV within the DFG Priority Program RATIO “Robust Argumentation Machines”.
In: Datenbank-Spektrum, Bd. 20, Nr. 2: S. 99-105
[PDF, 365kB]
Fromm, Michael; Faerman, Evgeniy und Seidl, Thomas
(2019):
TACAM: Topic And Context Aware Argument Mining.
In: 2019 Ieee/Wic/Acm International Conference on Web Intelligence (Wi 2019): S. 99-106
Kazempour, Daniyal und Seidl, Thomas
(2019):
On systematic hyperparameter analysis through the example of subspace clustering.
In: Scientific and Statistical Database Management (Ssdbm 2019): S. 226-229
Kazempour, Daniyal; Emmerig, Kilian; Kröger, Peer und Seidl, Thomas
(2019):
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform.
In: Scientific and Statistical Database Management (Ssdbm 2019): S. 222-225
Beer, Anna und Seidl, Thomas
(2019):
Graph Ordering and Clustering - A Circular Approach.
In: Scientific and Statistical Database Management (Ssdbm 2019): S. 185-188
Beer, Anna; Kazempour, Daniyal; Stephan, Lisa und Seidl, Thomas
(2019):
LUCK-Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function.
In: Scientific and Statistical Database Management (Ssdbm 2019): S. 181-184
Richter, Florian und Seidl, Thomas
(2019):
Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times.
In: Information Systems, Bd. 84: S. 265-282
Kauermann, Göran und Seidl, Thomas
(19. März 2018):
Data Science: a proposal for a curriculum.
In: International Journal of Data Science and Analytics
Kazempour, Daniyal; Beer, Anna; Herzog, Friederike; Kaltenthaler, Daniel; Lohrer, Johannes-Y. und Seidl, Thomas
(2018):
FATBIRD: A Tool for Flight and Trajectories Analyses of Birds.
In: 2018 Ieee 14Th International Conference on E-Science (E-Science 2018): S. 75-82
Lu, Yifeng und Seidl, Thomas
(2018):
Towards Efficient Closed Infrequent Itemset Mining using Bi-directional Traversing.
In: 2018 Ieee 5Th International Conference on Data Science and Advanced Analytics (Dsaa): S. 140-149
Hassani, Marwan und Seidl, Thomas
(2016):
Clustering Big Data streams: recent challenges and contributions.
In: It-information Technology, Bd. 58, Nr. 4: S. 206-213
Keim, Daniel A.; Kriegel, Hans-Peter und Seidl, Thomas
(1993):
Supporting Data mining of large databases by visual feedback queries.
In: Technical Report of the Institue for Computer Science, University of Munich, Nr. 9310
[PDF, 306kB]
Paper
Backofen, Rolf; Bry, François; Clote, Peter; Kriegel, Hans-Peter; Seidl, Thomas und Schulz, Klaus
(1. Oktober 1999):
Aktuelles Schlagwort Bioinformatik.
Informatik-Spektrum, 22
[PDF, 397kB]
Konferenzbeitrag
Gilhuber, Sandra; Busch, Julian; Rotthues, Daniel; Frey, Christian M. M. ORCID: https://orcid.org/0000-0003-2458-6651 und Seidl, Thomas ORCID: https://orcid.org/0000-0002-4861-1412
(2023):
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 18.- 22. September 2023.
Koutra, Danai; Plant, Claudia; Gomez Rodriguez, Manuel; Baralis, Elena und Bonchi, Francesco (Hrsg.):
In: Machine Learning and Knowledge Discovery in Databases: Research Track, Lecture Notes in Computer Science
Bd. 14169
Cham: Springer. S. 75-91
ORCID: https://orcid.org/0000-0003-2458-6651 und Seidl, Thomas ORCID: https://orcid.org/0000-0002-4861-1412
(2023):
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 18.- 22. September 2023.
Koutra, Danai; Plant, Claudia; Gomez Rodriguez, Manuel; Baralis, Elena und Bonchi, Francesco (Hrsg.):
In: Machine Learning and Knowledge Discovery in Databases: Research Track, Lecture Notes in Computer Science
Bd. 14169
Cham: Springer. S. 75-91
Gilhuber, Sandra; Hvingelby, Rasmus; Fok, Mang Ling Ada und Seidl, Thomas ORCID: https://orcid.org/0000-0002-4861-1412
(2023):
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 18.-22. September 2023.
Koutra, Danai; Plant, Claudia; Gomez Rodriguez, Manuel; Baralis, Elena und Bonchi, Francesco (Hrsg.):
In: Machine Learning and Knowledge Discovery in Databases: Research Track, Lecture Notes in Artificial Intelligence
Bd. 14170
Cham: Springer. S. 330-347
ORCID: https://orcid.org/0000-0002-4861-1412
(2023):
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Turin, Italy, 18.-22. September 2023.
Koutra, Danai; Plant, Claudia; Gomez Rodriguez, Manuel; Baralis, Elena und Bonchi, Francesco (Hrsg.):
In: Machine Learning and Knowledge Discovery in Databases: Research Track, Lecture Notes in Artificial Intelligence
Bd. 14170
Cham: Springer. S. 330-347
Zhang, Yao; Ma, Yunpu ORCID: https://orcid.org/0000-0001-6112-8794; Seidl, Thomas und Tresp, Volker ORCID: https://orcid.org/0000-0001-9428-3686
(2023):
Adaptive Multi-Resolution Attention with Linear Complexity.
2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 18-23 June 2023.
In: IJCNN 2023, International Joint Conference on Neural Networks, 18-23 June 2023, Gold Coast Convention and Exhibition Centre, Queensland, Australia,
Piscataway, NJ, USA: IEEE.
ORCID: https://orcid.org/0000-0001-6112-8794; Seidl, Thomas und Tresp, Volker ORCID: https://orcid.org/0000-0001-9428-3686
(2023):
Adaptive Multi-Resolution Attention with Linear Complexity.
2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia, 18-23 June 2023.
In: IJCNN 2023, International Joint Conference on Neural Networks, 18-23 June 2023, Gold Coast Convention and Exhibition Centre, Queensland, Australia,
Piscataway, NJ, USA: IEEE.
Winkel, David ORCID: https://orcid.org/0000-0001-8829-0863; Strauß, Niklas ORCID: https://orcid.org/0000-0002-8083-7323; Schubert, Matthias ORCID: https://orcid.org/0000-0002-6566-6343 und Seidl, Thomas ORCID: https://orcid.org/0000-0002-4861-1412
(2023):
Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection.
European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022.
In: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings,
Bd. 13718
Cham: Springer. S. 185-200
ORCID: https://orcid.org/0000-0001-8829-0863; Strauß, Niklas ORCID: https://orcid.org/0000-0002-8083-7323; Schubert, Matthias ORCID: https://orcid.org/0000-0002-6566-6343 und Seidl, Thomas ORCID: https://orcid.org/0000-0002-4861-1412
(2023):
Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection.
European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022.
In: Machine Learning and Knowledge Discovery in Databases European Conference, ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings,
Bd. 13718
Cham: Springer. S. 185-200
Wimbauer, Anna; Richter, Florian und Seidl, Thomas
(2022):
PErrCas: Process Error Cascade Mining in Trace Streams.
ICPM 2021 International Workshops, Eindhoven, The Netherlands,, October 31 – November 4, 2021.
Munoz-Gama, Jorge und Lu, Xixi (Hrsg.):
In: Process Mining Workshops. ICPM 2021 International Workshops, Eindhoven, The Netherlands, October 31 – November 4, 2021, Revised Selected Papers, Lecture Notes in Business Information Processing
Bd. 433
S. 224-236
Gilhuber, Sandra; Jahn, Philipp; Ma, Yunpu und Seidl, Thomas
(2022):
VERIPS: Verified Pseudo-label Selection for Deep Active Learning.
2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 28 November 2022 - 01 December 2022.
In: 2022 IEEE International Conference on Data Mining (ICDM),
S. 951-956
Fromm, Michael; Faerman, Evgeniy; Berrendorf, Max; Bhargava, Siddharth; Qi, Ruoxia; Zhang, Yao; Dennert, Lukas; Selle, Sophia; Mao, Yang und Seidl, Thomas
(2021):
Argument Mining Driven Analysis of Peer-Reviews.
The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Virtual Event, February 2-9, 2021.
In: Proceedings of the AAAI Conference on Artificial Intelligence,
Bd. 35, Nr. 6
AAAI Press. S. 4758-4766
Beer, Anna; Stephan, Lisa und Seidl, Thomas
(2021):
LUCKe — Connecting Clustering and Correlation Clustering.
2021 International Conference on Data Mining Workshops (ICDMW), Auckland, New Zealand, 07-10 December 2021.
In: 2021 International Conference on Data Mining Workshops (ICDMW),
New York: IEEE. S. 431-440
Busch, Julian; Hunemorder, Maximilian; Held, Janis; Kroger, Peer und Seidl, Thomas
(2021):
Implicit Hough Transform Neural Networks for Subspace Clustering.
2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, 07-10 December 2021.
In: 2021 International Conference on Data Mining Workshops (ICDMW),
New York: IEEE. S. 441-448
Zellner, Ludwig; Sontheim, Janina; Richter, Florian; Lindner, Gabriel und Seidl, Thomas
(2021):
SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size.
2021 International Conference on Data Mining Workshops (ICDMW), Auckland, New Zealand, 07-10 December 2021.
In: 2021 International Conference on Data Mining Workshops (ICDMW),
New York: IEEE. S. 925-934
Hassani, Marwan; Lu, Yifeng; Wischnewsky, Jens und Seidl, Thomas
(2016):
A Geometric Approach for Mining Sequential Patterns in Interval-Based Data Streams.
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, 24-29 July 2016.
IEEE Computer Society. S. 2128-2135
Scharwächter, Erik; Müller, Emmanuel; Donges, Jonathan; Hassani, Marwan und Seidl, Thomas
(2016):
Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining.
IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Catalonia, Spain, 12-15 Dec. 2016.
IEEE Computer Society. S. 1191-1196
Aldinger, Kai; Ester, Martin; Förstner, Gabriele; Kriegel, Hans-Peter und Seidl, Thomas
(1994):
Datenbankenunterstützung für das Protein-Protein-Docking: ein effizienter und robuster Feature-Index.
2. GI-Fachtagung “Informatik in den Biowissenschaften”, Jena, 05. - 07.09.1994.
In: Bioinformatik – Computereinsatz in den Biowissenschaften,
S. 41-52
[PDF, 157kB]