Mastering Machine Learning with R - Second Edition

PAM

For Partitioning Around Medoids, let's first define a medoid.

A medoid is an observation of a cluster that minimizes the dissimilarity (in our case, calculated using the Gower metric) between the other observations in that cluster. So, similar to k-means, if you specify five clusters, you will have five partitions of the data.

With the objective of minimizing the dissimilarity of all the observations to the nearest medoid, the PAM algorithm iterates over the following steps:

1. Randomly select k observations as the initial medoid.
2. Assign each observation to the closest medoid.
3. Swap each medoid and non-medoid observation, computing the dissimilarity cost.
4. Select the configuration that minimizes the total dissimilarity.
5. Repeat steps 2 through ...