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Gruppiert nach: Dokumententyp | Veröffentlichungsdatum
Anzahl der Publikationen: 29

Zeitschriftenartikel

Ullmann, Theresa ORCID logoORCID: https://orcid.org/0000-0003-1215-8561; Beer, Anna ORCID logoORCID: https://orcid.org/0000-0002-6890-997X; Hünemörder, Maximilian ORCID logoORCID: https://orcid.org/0000-0001-9848-3714; Seidl, Thomas und Boulesteix, Anne-Laure ORCID logoORCID: 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]

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

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]

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

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; 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

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

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; 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; 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

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

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

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

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]

Diese Liste wurde am Sat Mar 23 22:03:58 2024 CET erstellt.