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A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data

Author

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  • Guidi, Lionel
  • Ibanez, Frédéric
  • Calcagno, Vincent
  • Beaugrand, Grégory

Abstract

Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present a new criterion to determine statistically the optimal level of partition in a classification tree. The criterion robustness is tested against perturbated data (outliers) using an observation or variable with values randomly generated. The technique, called Random Simulation Test (RST), is tested on (1) the well-known Iris dataset [Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper [Milligan, G.W., Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179] and finally (3) is applied on real copepod communities data previously analyzed in Beaugrand et al. [Beaugrand, G., Ibanez, F., Lindley, J.A., Reid, P.C., 2002. Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195]. The technique is compared to several standard techniques. RST performed generally better than existing algorithms on simulated data and proved to be especially efficient with highly variable datasets.

Suggested Citation

  • Guidi, Lionel & Ibanez, Frédéric & Calcagno, Vincent & Beaugrand, Grégory, 2009. "A new procedure to optimize the selection of groups in a classification tree: Applications for ecological data," Ecological Modelling, Elsevier, vol. 220(4), pages 451-461.
    • Handle: RePEc:eee:ecomod:v:220:y:2009:i:4:p:451-461
    DOI: 10.1016/j.ecolmodel.2008.11.006
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    References listed on IDEAS

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    1. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    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. Bertrand, P. & Bel Mufti, G., 2006. "Loevinger's measures of rule quality for assessing cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 992-1015, February.
    4. Glenn Milligan, 1981. "A monte carlo study of thirty internal criterion measures for cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 46(2), pages 187-199, June.
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