LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets

Summary: LOG-Means estimates the optimal number of clusters with sublinear dependence on the search space, enabling fast tuning on large datasets and Spark. In Apache Spark experiments, it outperforms 13 baselines in runtime and accuracy, delivering the most systematic large-space comparison to date. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12291
Venue: VLDB
Year: 2020
Pagerank: 5.3251649e-05
Overall Rank: 9,075 | 37.74%
DOI: 10.14778/3407790.3407813

Incoming Non-self Citations Over Time

Authors

1. Manuel Fritz (University of Stuttgart)
2. Michael Behringer (University of Stuttgart)
3. Holger Schwarz (University of Stuttgart)

BibTeX Citation

@article{fritz_vldb20,
        title = {{LOG-Means: Efficiently Estimating the Number of Clusters in Large Datasets}},
        author = {Fritz, Manuel and Behringer, Michael and Schwarz, Holger},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2118--2131},
        doi = {10.14778/3407790.3407813},
        url = {https://doi.org/10.14778/3407790.3407813},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 3 of 3 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,959	Federated and Balanced Clustering for High-dimensional Data	2025	VLDB	5.093636e-05
11,253	Ensemble Clustering based on Meta-Learning and Hyperparameter Optimization	2024	VLDB	5.093636e-05
13,387	ML2DAC: Meta-learning to Democratize AutoML for Clustering Analyses	2023	SIGMOD	-

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
2,085	Scalable K-Means++	2012	VLDB	9.1943614e-05
4,366	Solving k-center Clustering (with Outliers) in MapReduce and Streaming, almost as Accurately as Sequentially	2019	VLDB	6.7407122e-05

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