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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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 270 | OPTICS: Ordering Points To Identify the Clustering Structure | 1999 | SIGMOD | 0.00029505642 |
| 961 | DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation | 2015 | SIGMOD | 0.00015001792 |
| 4,763 | SCAR — Spectral Clustering Accelerated and Robustified | 2022 | VLDB | 5.9395463e-05 |
| 9,053 | LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets | 2020 | VLDB | 4.4039656e-05 |
| 13,184 | ML2DAC: Meta-learning to Democratize AutoML for Clustering Analyses | 2023 | SIGMOD | - |
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