Question

Defination k-means vs k-medoids algorithm

Answer 1

K-medoids minimizes an arbitrarily chosen distance (not necessarily an absolute distance) between clustered elements and the medoid. Actually thepammethod (an example implementation of K-medoids in R) used above, by default uses the Euclideandistance as a metric. K-means always uses the squared Euclidean. The medoids in K-medoids are chosen out of the cluster elements, not out of a whole points space as centroids in K-means.