Hierarchical clustering of continuous variables based on the empirical copula process and permutation linkages
The agglomerative hierarchical clustering of continuous variables is studied in the framework of the likelihood linkage analysis method proposed by Lerman. The similarity between variables is defined from the process comparing the empirical copula with the independence copula in the spirit of the test of independence proposed by Deheuvels. Unlike more classical similarity coefficients for variables based on rank statistics, the comparison measure considered in this work can also be sensitive to non-monotonic dependencies. As aggregation criteria, besides classical linkages, permutation-based linkages related to procedures for combining dependent p-values are considered. The performances of the corresponding clustering algorithms are compared through thorough simulations. In order to guide the choice of a partition, a natural probabilistic selection strategy, related to the use of the gap statistic in object clustering, is proposed and empirically compared with classical ordinal approaches. The resulting variable clustering procedure can be equivalently regarded as a potentially less computationally expensive alternative to more powerful tests of multivariate independence.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Loughin, Thomas M., 2004. "A systematic comparison of methods for combining p-values from independent tests," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 467-485, October.
- Kojadinovic, Ivan, 2004. "Agglomerative hierarchical clustering of continuous variables based on mutual information," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 269-294, June.
- Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer, vol. 50(2), pages 159-179, June.
- Plasse, Marie & Niang, Ndeye & Saporta, Gilbert & Villeminot, Alexandre & Leblond, Laurent, 2007. "Combined use of association rules mining and clustering methods to find relevant links between binary rare attributes in a large data set," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 596-613, September.
- Beran, R. & Bilodeau, M. & Lafaye de Micheaux, P., 2007. "Nonparametric tests of independence between random vectors," Journal of Multivariate Analysis, Elsevier, vol. 98(9), pages 1805-1824, October.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:90-108. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If references are entirely missing, you can add them using this form.