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On the number of groups in clustering

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  • Fischer, Aurélie

Abstract

Clustering is the problem of partitioning data into a finite number k of homogeneous and separate groups, called clusters. A good choice of k is essential for building meaningful clusters. In this paper, this task is addressed from the point of view of model selection via penalization. We design an appropriate penalty shape and derive an associated oracle-type inequality. The method is illustrated on both simulated and real-life data sets.

Suggested Citation

  • Fischer, Aurélie, 2011. "On the number of groups in clustering," Statistics & Probability Letters, Elsevier, vol. 81(12), pages 1771-1781.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:12:p:1771-1781
    DOI: 10.1016/j.spl.2011.07.005
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    References listed on IDEAS

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    1. Hardy, Andre, 1996. "On the number of clusters," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 83-96, November.
    2. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    3. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    4. Sugar, Catherine A. & James, Gareth M., 2003. "Finding the Number of Clusters in a Dataset: An Information-Theoretic Approach," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 750-763, January.
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    Cited by:

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    2. Chakraborty, Saptarshi & Das, Swagatam, 2018. "Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 148-156.

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