Partitioning Around Medoids
Partitioning Around Medoids is an unsupervised machine learning algorithm for clustering analysis
The PAM algorithm searches for k representative objects in a data set (k medoids) and then assigns each object to the closest medoid in order to create clusters. Its aim is to minimize the sum of dissimilarities between the objects in a cluster and the center of the same cluster (medoid). It is known to be a robust version of k-means as it is considered to be less sensitive to outliers.
The Partitioning Around Medoids implemented in XLSTAT-R calls the pam function from the cluster package in R (Martin Maechler, Peter Rousseeuw, Anja Struyf, Mia Hubert). The function offers as well a useful tool to determine the number of k called the silhouette plot.