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: 6647
Venue: SIGMOD
Year: 2023
Pagerank: -
Overall Rank: 13,184 | 8.29%
DOI: 10.1145/3589289

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,045	Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization	2024	VLDB	4.1945683e-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
9,053	LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets	2020	VLDB	4.4039656e-05

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