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A non-parametric method to estimate the number of clusters

Author

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  • Fujita, André
  • Takahashi, Daniel Y.
  • Patriota, Alexandre G.

Abstract

An important and yet unsolved problem in unsupervised data clustering is how to determine the number of clusters. The proposed slope statistic is a non-parametric and data driven approach for estimating the number of clusters in a dataset. This technique uses the output of any clustering algorithm and identifies the maximum number of groups that breaks down the structure of the dataset. Intensive Monte Carlo simulation studies show that the slope statistic outperforms (for the considered examples) some popular methods that have been proposed in the literature. Applications in graph clustering, in iris and breast cancer datasets are shown.

Suggested Citation

  • Fujita, André & Takahashi, Daniel Y. & Patriota, Alexandre G., 2014. "A non-parametric method to estimate the number of clusters," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 27-39.
    • Handle: RePEc:eee:csdana:v:73:y:2014:i:c:p:27-39
    DOI: 10.1016/j.csda.2013.11.012
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    References listed on IDEAS

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    4. Fang, Yixin & Wang, Junhui, 2012. "Selection of the number of clusters via the bootstrap method," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 468-477.
    5. Celeux, Gilles & Govaert, Gerard, 1992. "A classification EM algorithm for clustering and two stochastic versions," Computational Statistics & Data Analysis, Elsevier, vol. 14(3), pages 315-332, October.
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    Cited by:

    1. Fujita, André & Takahashi, Daniel Yasumasa & Balardin, Joana Bisol & Vidal, Maciel Calebe & Sato, João Ricardo, 2017. "Correlation between graphs with an application to brain network analysis," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 76-92.
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    3. Jonas M. B. Haslbeck & Dirk U. Wulff, 2020. "Estimating the number of clusters via a corrected clustering instability," Computational Statistics, Springer, vol. 35(4), pages 1879-1894, December.
    4. Vainora, J., 2024. "Latent Position-Based Modeling of Parameter Heterogeneity," Cambridge Working Papers in Economics 2455, Faculty of Economics, University of Cambridge.

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