What makes the distance measure in k-medoid “better” than k-means?

Question

I am reading about the difference between k-means clustering and k-medoid clustering.

Supposedly there is an advantage to using the pairwise distance measure in the k-me

Answer 1

Just a tiny note added to @Eli's answer, K-medoid is more robust to noise and outliers than k-means because the latter selects the cluster center, which is mostly just a "virtue point", on the other hand the former chooses the "actual object" from the cluster.

Suppose you have five 2D points in one cluster with the coordinates of (1,1),(1,2),(2,1),(2,2), and (100,100). If we don't consider the object exchanges among the clusters, with k-means you will get the center of cluster (21.2,21.2) which is pretty distracted by the point (100,100). However, with k-medoid will choose the center among (1,1),(1,2),(2,1),and (2,2) according to its algorithm.

Here is a fun applet ( E.M. Mirkes, K-means and K-medoids applet. University of Leicester, 2011 ) that you can randomly generate dataset in the 2D plane and compare k-medoid and k-means learning process.