Partitioning around the actual center - K-medoids clustering

K-medoids is a partitioning clustering algorithm related to the K-means algorithm. Given a set of n objects and a k number that determines how many clusters you want to output, K-medoids divides the dataset into groups, trying to minimize the average quadratic error, the distance between the points of a cluster, and the point designated to be the center.

What is a medoid?

Unlike K-means, where this point is artificial (that is, the pure average of all points in the cluster also named centroid), in K-medoids, the point used is placed more centrally; so the center is one of the actual data points (medoid). A medoid can be defined as an object of a cluster whose average...