Dennis Treder-Tschechlov

Orcid: 0000-0002-2502-4215

According to our database1, Dennis Treder-Tschechlov authored at least 12 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

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Bibliography

2024
Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization.
Proc. VLDB Endow., July, 2024

Empowering Domain Experts to Enhance Clustering Results Through Interactive Refinement.
Proceedings of the Database Systems for Advanced Applications, 2024

2023
Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification.
VLDB J., September, 2023

ML2DAC: Meta-Learning to Democratize AutoML for Clustering Analysis.
Proc. ACM Manag. Data, 2023

SDRank: A Deep Learning Approach for Similarity Ranking of Data Sources to Support User-Centric Data Analysis.
Proceedings of the 25th International Conference on Enterprise Information Systems, 2023

Approach to Synthetic Data Generation for Imbalanced Multi-class Problems with Heterogeneous Groups.
Proceedings of the Datenbanksysteme für Business, 2023

2022
Efficient exploratory clustering analyses in large-scale exploration processes.
VLDB J., 2022

Increasing Explainability of Clustering Results for Domain Experts by Identifying Meaningful Features.
Proceedings of the 24th International Conference on Enterprise Information Systems, 2022

2021
AutoML4Clust: Efficient AutoML for Clustering Analyses.
Proceedings of the 24th International Conference on Extending Database Technology, 2021

Efficient Exploratory Clustering Analyses with Qualitative Approximations.
Proceedings of the 24th International Conference on Extending Database Technology, 2021

2020
Learning from Past Observations: Meta-Learning for Efficient Clustering Analyses.
Proceedings of the Big Data Analytics and Knowledge Discovery, 2020

2019
Towards a Collaborative Repository for the Documentation of Service-Based Antipatterns and Bad Smells.
Proceedings of the IEEE International Conference on Software Architecture Companion, 2019


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