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K-means properties on six clustering benchmark datasets

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Abstract

This paper has two contributions. First, we introduce a clustering basic benchmark. Second, we study the performance of k-means using this benchmark. Specifically, we measure how the performance depends on four factors: (1) overlap of clusters, (2) number of clusters, (3) dimensionality, and (4) unbalance of cluster sizes. The results show that overlap is critical, and that k-means starts to work effectively when the overlap reaches 4% level.

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Notes

  1. https://archive.ics.uci.edu/ml/datasets.html?format=&task=clu

  2. https://www.uni-marburg.de/fb12/arbeitsgruppen/datenbionik/data

  3. https://ifcs.boku.ac.at/repository/

  4. https://github.com/deric/clustering-benchmark

  5. http://www.gagolewski.com/resources/data/clustering/

  6. https://data.world/datasets/clustering

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Authors and Affiliations

  1. Machine Learning Group, School of Computing, University of Eastern Finland, P.O. Box 111, FIN-80101, Joensuu, Finland

    Pasi Fränti & Sami Sieranoja

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  1. Pasi Fränti
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  2. Sami Sieranoja
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Correspondence to Pasi Fränti.

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Fränti, P., Sieranoja, S. K-means properties on six clustering benchmark datasets. Appl Intell 48, 4743–4759 (2018). https://doi.org/10.1007/s10489-018-1238-7

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