*3.2.2 k-medoids*

K-medoids is a partitional clustering algorithm proposed in 1987 by Kaufman and Rousseeuw. It is a variant of the K-means algorithm that is less sensitive to noise and outliers because it uses medoids as cluster centers instead of means that are easily influenced by extreme values. Medoids are the most centrally located objects of the clusters, with a minimum sum of distances to other points. After searching for k representative objects in a data set, the algorithm which is called Partitioning Around Medoids (PAM) assigns each object to the closest medoid in order to create clusters. Like in k-means the number of classes to be generated needs to be specified.
