Kaufman and Rousseeuw (1990)), which estimates the "cost" of a cluster as the sum of squared distances between each vector and the cluster centroid Under this view, a good cluster is one with a low cost, and the goal of the clustering algorithm is to minimise the average distance to the centroid.
for our purposes it is more convenient for a good cluster to have a high rating.