Last updated: 2024-08-16
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The Partitioning Around Medoids (PAM) methods is as follows:
Each time the algorithm is run, different initial seeds will be
picked in Step 2, and in general, this can lead to different final
results. A popular implementation is the pam() function in
the {cluster} package.
A slight variation of the method replaces the medoids by the
arithematic means (centers of gravity) of the clusters and is called
k-means. Whereas in PAM the centers are observations, this is
not, in general, the case with k-means. The function
kmeans() comes with base R via the {stats} package.
These so-called k-methods are the most common off-the-shelf methods for clustering; they work particularly well when the clusters are of comparable size and convex (blob-shaped). On the other hand, if the true clusters are very different in size, the larger ones will tend to be broken up; the same is true for groups that have pronounced non-spherical or non-elliptical shapes.
There are clever schemes that repeat the process many times using different initial centers or resampled datasets. Repeating a clustering procedure multiple times on the same data but with different starting points creates strong forms. Repeatedly subsampling the dataset and applying a clustering method will result in groups of observations that are “almost always” grouped together; these are called tight clusters. The study of strong forms or tight clusters facilitates the choice of the number of clusters. The {clusterExperiment} package can combine and compare the output from many different clusterings.
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
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time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
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