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A Simple Permutation Test for Clusteredness

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  • Michael Greenacre

Abstract

Hierarchical clustering is a popular method for finding structure in multivariate data, resulting in a binary tree constructed on the particular objects of the study, usually sampling units. The user faces the decision where to cut the binary tree in order to determine the number of clusters to interpret and there are various ad hoc rules for arriving at a decision. A simple permutation test is presented that diagnoses whether non-random levels of clustering are present in the set of objects and, if so, indicates the specific level at which the tree can be cut. The test is validated against random matrices to verify the type I error probability and a power study is performed on data sets with known clusteredness to study the type II error.

Suggested Citation

  • Michael Greenacre, 2015. "A Simple Permutation Test for Clusteredness," Working Papers 555, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:555
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    References listed on IDEAS

    as
    1. Michael Greenacre, 2008. "Correspondence analysis of raw data," Economics Working Papers 1112, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
    2. Gordon, A. D., 1994. "Identifying genuine clusters in a classification," Computational Statistics & Data Analysis, Elsevier, vol. 18(5), pages 561-581, December.
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    Cited by:

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    2. Lucie Aulus-Giacosa & Sébastien Ollier & Cleo Bertelsmeier, 2024. "Non-native ants are breaking down biogeographic boundaries and homogenizing community assemblages," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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