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. This package implements 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 containing randomly generated values.
|Author||Sakina-Dorothée Ayata, Vincent Calgano, Lionel Guidi, Jean-Olivier Irisson|
|Maintainer||Jean-Olivier Irisson <[email protected]>|
|Package repository||View on GitHub|
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