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ORCIDDennis Treder-Tschechlov
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2020 – today
- 2024
[j4]Dennis Treder-Tschechlov, Manuel Fritz, Holger Schwarz, Bernhard Mitschang:
Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization. Proc. VLDB Endow. 17(11): 2880-2892 (2024)- [c8]Michael Behringer, Dennis Treder-Tschechlov, Jannis Rapp:
Empowering Domain Experts to Enhance Clustering Results Through Interactive Refinement. DASFAA (7) 2024: 518-522 - 2023
- [j3]Dennis Treder-Tschechlov
, Manuel Fritz, Holger Schwarz, Bernhard Mitschang:
ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis. Proc. ACM Manag. Data 1(2): 144:1-144:26 (2023) - [j2]Vitali Hirsch, Peter Reimann, Dennis Treder-Tschechlov, Holger Schwarz, Bernhard Mitschang:
Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification. VLDB J. 32(5): 1037-1064 (2023) - [c7]Dennis Treder-Tschechlov, Peter Reimann, Holger Schwarz, Bernhard Mitschang:
Approach to Synthetic Data Generation for Imbalanced Multi-class Problems with Heterogeneous Groups. BTW 2023: 329-351 - [c6]Michael Behringer, Dennis Treder-Tschechlov, Julius Voggesberger, Pascal Hirmer, Bernhard Mitschang:
SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis. ICEIS (1) 2023: 419-428 - 2022
- [j1]Manuel Fritz, Michael Behringer, Dennis Tschechlov, Holger Schwarz:
Efficient exploratory clustering analyses in large-scale exploration processes. VLDB J. 31(4): 711-732 (2022) - [c5]Michael Behringer, Pascal Hirmer, Dennis Tschechlov, Bernhard Mitschang:
Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features. ICEIS (2) 2022: 364-373 - 2021
- [c4]Manuel Fritz, Dennis Tschechlov, Holger Schwarz:
Efficient Exploratory Clustering Analyses with Qualitative Approximations. EDBT 2021: 337-342 - [c3]Dennis Tschechlov, Manuel Fritz, Holger Schwarz:
AutoML4Clust: Efficient AutoML for Clustering Analyses. EDBT 2021: 343-348 - 2020
- [c2]Manuel Fritz, Dennis Tschechlov, Holger Schwarz:
Learning from Past Observations: Meta-Learning for Efficient Clustering Analyses. DaWaK 2020: 364-379
2010 – 2019
- 2019
- [c1]Justus Bogner, Tobias Boceck, Matthias Popp, Dennis Tschechlov, Stefan Wagner, Alfred Zimmermann:
Towards a Collaborative Repository for the Documentation of Service-Based Antipatterns and Bad Smells. ICSA Companion 2019: 95-101
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