kmedoid
K-medoids, also known as k-medoids clustering, is a partitioning clustering algorithm that partitions a dataset into k clusters by minimizing the sum of dissimilarities between points and their cluster medoids. A medoid is the object in a cluster whose average dissimilarity to all other objects in the cluster is minimal; in other words, it is the most centrally located observation in the cluster. Unlike k-means, which uses centroids that may not correspond to actual data points, k-medoids uses real observations as centers, making it more robust to outliers and compatible with arbitrary dissimilarity measures.
A canonical approach is Partitioning Around Medoids (PAM). The algorithm initializes by selecting k medoids. It
Variants such as CLARA (Clustering Large Applications) and CLARANS (Clustering Large Applications with Nearest neighbor Search)
Properties and use cases: k-medoids can use any dissimilarity metric, including non-Euclidean or categorical distances, enabling