Dennis Treder-Tschechlov

Zitationsmetriken

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Publikationen

2023

Treder-Tschechlov, Dennis; Fritz, Manuel; Schwarz, Holger; Bernhard Mitschang: "ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis". In Proceedings of the ACM on Management of Data 2023 (SIGMOD 2023).

Treder-Tschechlov, Dennis; Reimann, Peter; Schwarz, Holger; Mitschang, Bernhard: "Approach to Synthetic Data Generation for Imbalanced Multi-class Problems with Heterogeneous Groups". BTW 2023.

Hirsch, Vitali; Reimann, Peter; Treder-Tschechlov, Dennis; Schwarz, Holger; Mitschang, Bernhard: "Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification". The VLDB Journal (2023).

Behringer, Michael; Treder-Tschechlov, Dennis; Voggesberger, Julius; Hirmer, Pascal and Mitschang, Bernhard: "SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis". In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023);

2022

Behringer, Michael; Hirmer, Pascal; Tschechlov, Dennis; Mitschang, Bernhard: "Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features". In Proceedings of the 24th International Conference on Enterprise Information Systems (ICEIS 2022);

2021

Fritz, Manuel; Behringer, Michael; Tschechlov, Dennis; Schwarz, Holger: "Efficient exploratory clustering analyses in large-scale exploration processes". The VLDB Journal (2021).

Tschechlov, Dennis; Fritz, Manuel; Schwarz, Holger: “AutoML4Clust: Efficient AutoML for Clustering Analyses”, in Proceedings of the International Conference on Extending Database Technology (EDBT 2021).

Fritz, Manuel; Tschechlov, Dennis; Schwarz, Holger: “Efficient Exploratory Clustering Analyses with Qualitative Approximations”, in Proceedings of the International Conference on Extending Database Technology (EDBT 2021).

2020

Fritz, Manuel; Tschechlov, Dennis; Schwarz, Holger: “Learning from past observations: Meta-Learning for Efficient Clustering Analyses”, in Proceedings of 22nd International Conference on Big Data Analytics and Knowledge Discovery (DaWaK 2020). Lecture Notes in Computer Science (Vol. 12393 LNCS), Springer, Cham, pp. 364-379.

2019

Tschechlov, D. (2019). "Analysis and Transfer of AutoML Concepts for Clustering Algorithms" (Master's thesis). http://dx.doi.org/10.18419/opus-10755.

J. Bogner, T. Boceck, M. Popp, D. Tschechlov, S. Wagner and A. Zimmermann, "Towards a Collaborative Repository for the Documentation of Service-Based Antipatterns and Bad Smells," 2019 IEEE International Conference on Software Architecture Companion (ICSA-C), 2019, pp. 95-101, doi: 10.1109/ICSA-C.2019.00025.

Laufend:

• AutoML for clustering to partition data for a multi-class use case, Bachelor Thesis, 2023.
• Refinement of Partitioning for Multi-class Problems with Heterogeneous Groups, Master Thesis, 2023.

Abgeschlossen:

• Analysis and Integration of Data Preprocessing Steps in AutoML for Clustering, Bachelor Thesis, 2023.
• Empowering Domain Experts to Interactively Refine of Clustering Results, Master Thesis, 2022.
• Extraction of Hierarchical Domain Knowledge for Complex Multiclass Problems, Master Thesis, 2022.
• Data-driven Partitioning of Training Data for Complex Multiclass Problems, Master Thesis, 2022.
• Enhancement for the UI of Custom Data Generation and Machine Learning, Practical Course, 2022.
• Partitioning Training Data for Complex Multi-class Problems using Constraint-based Clustering, Master Thesis, 2021.
• Analysis and Comparison of Constrained-based Clustering algorithms, SWT- Research Project, 2021.
• Analysis of Data-driven methods to detect Incorrectly Documented Class Label, Bachelor Thesis, 2021.
• Analysis of Clustering Algorithms to Partition Training Data for Complex Multiclass Problems, Bachelor Thesis, 2021.
• Visualization of Complex Synthetic Data Generation, Practical Course, 2021.
• Analysis and Comparison of Ensemble Methods for Clustering Analyses, Bachelor Theis, 2021.

Software Campus Projekt VALID-PARTITION