I want to perform K-means clustering on objects I have, but the objects aren't described as points in space, i.e. by objects x features dataset. However, I am able to compute the distance between any two objects (it is based on a similarity function). So, I dispose of the distance matrix objects x objects.
I've implemented K-means before, but that was with points dataset input; and with distance matrix input it's not clear to me how to update the clusters to be the cluster "centers" without a point-representation. How would this normally be done? Are there versions of K-means or methods close to it, for that?
Best Answer
Obviously, k-means needs to be able to compute means.
However, there is a well-known variation of it known as k-medoids or PAM (Partitioning Around Medoids), where the medoid is the existing object most central to the cluster. K-medoids only needs the pairwise distances.