Last updated: 2024-08-16
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| Rmd | ad9ead7 | Dave Tang | 2024-08-16 | Hierarchical clustering |
Hierarchical clustering is a bottom-up approach, by which similar observations and sub-classes are assembled iteratively. The order of the labels does not matter within sibling pairs. Horizontal distances are usually meaningless, while the vertical distances do encode some information. These properties are important to remember when making interpretations about neighbours that are not monophyletic (i.e., not in the same subtree or clade), but appear as neighbours in the plot.
An alternative, top-down, approach takes all the objects and splits them sequentially according to a chosen criterion. Such so-called recursive partitioning methods are often used to make decision trees. They can be useful for prediction: the goal is to split heterogeneous populations into more homogeneous subgroups by partitioning.
sessionInfo()R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
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[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
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[1] stats graphics grDevices utils datasets methods base
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