Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization
Summary: EffEns uses meta-learning to predict dataset characteristics and the mapping between generated base clusterings and consensus-function effectiveness, enabling targeted, efficient ensemble generation. Then selects and hyperparameter-optimizes a consensus function, yielding faster and more accurate ensembles than prior methods. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Dennis Treder-Tschechlov (University of Stuttgart)
- 2. Manuel Fritz (University of Stuttgart)
- 3. Holger Schwarz (University of Stuttgart)
- 4. Bernhard Mitschang (University of Stuttgart)
BibTeX Citation
@article{tredertschechlov_vldb24,
title = {{Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization}},
author = {Treder-Tschechlov, Dennis and Fritz, Manuel and Schwarz, Holger and Mitschang, Bernhard},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {2880--2892},
doi = {10.14778/3681954.3681970},
url = {https://doi.org/10.14778/3681954.3681970},
year = {2024}
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 291 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00022264197 |
| 962 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00012936472 |
| 5,380 | SCAR — Spectral Clustering Accelerated and Robustified | 2022 | VLDB | 6.2397041e-05 |
| 9,075 | LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets | 2020 | VLDB | 5.3251649e-05 |
| 13,387 | ML2DAC: Meta-learning to Democratize AutoML for Clustering Analyses | 2023 | SIGMOD | - |
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