A new procedure to optimize the selection of groups in a classification tree
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...
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ftplymouthml:oai:plymsea.ac.uk:5750 2023-05-15T17:33:51+02:00 A new procedure to optimize the selection of groups in a classification tree Guidi, L 2009 http://plymsea.ac.uk/id/eprint/5750/ unknown Guidi, L. 2009 A new procedure to optimize the selection of groups in a classification tree. Ecological Modelling, 220 (4). 451-461. Publication - Article PeerReviewed 2009 ftplymouthml 2022-09-13T05:48:23Z 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. Article in Journal/Newspaper North Atlantic Copepods Plymouth Marine Science Electronic Archive (PlyMSEA - Plymouth Marine Laboratory, PML) Lindley ENVELOPE(159.100,159.100,-81.767,-81.767) |
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Plymouth Marine Science Electronic Archive (PlyMSEA - Plymouth Marine Laboratory, PML) |
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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. |
format |
Article in Journal/Newspaper |
author |
Guidi, L |
spellingShingle |
Guidi, L A new procedure to optimize the selection of groups in a classification tree |
author_facet |
Guidi, L |
author_sort |
Guidi, L |
title |
A new procedure to optimize the selection of groups in a classification tree |
title_short |
A new procedure to optimize the selection of groups in a classification tree |
title_full |
A new procedure to optimize the selection of groups in a classification tree |
title_fullStr |
A new procedure to optimize the selection of groups in a classification tree |
title_full_unstemmed |
A new procedure to optimize the selection of groups in a classification tree |
title_sort |
new procedure to optimize the selection of groups in a classification tree |
publishDate |
2009 |
url |
http://plymsea.ac.uk/id/eprint/5750/ |
long_lat |
ENVELOPE(159.100,159.100,-81.767,-81.767) |
geographic |
Lindley |
geographic_facet |
Lindley |
genre |
North Atlantic Copepods |
genre_facet |
North Atlantic Copepods |
op_relation |
Guidi, L. 2009 A new procedure to optimize the selection of groups in a classification tree. Ecological Modelling, 220 (4). 451-461. |
_version_ |
1766132485159124992 |