ML2DAC: Meta-learning to Democratize AutoML for Clustering Analyses

Summary: ML2DAC leverages meta-learning from prior clustering evaluations to pick a suitable cluster validity index, efficiently select algorithm/hyperparameters, and prune the search space. It outperforms SOTA in accuracy and runtime for unsupervised clustering. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6709
Venue: SIGMOD
Year: 2023
Pagerank: -
Overall Rank: 13,387 | 8.16%
DOI: 10.1145/3589289

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

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

@inproceedings{tredertschechlov_sigmod23,
        title = {{ML2DAC: Meta-learning to Democratize AutoML for Clustering Analyses}},
        author = {Treder-Tschechlov, Dennis and Fritz, Manuel and Schwarz, Holger and Mitschang, Bernhard},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3589289},
        url = {https://dl.acm.org/doi/10.1145/3589289},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,253	Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization	2024	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
9,075	LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets	2020	VLDB	5.3251649e-05

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