In this final article in my mini-series on k-means and its variants, I will talk about the k-medoids algorithm, also commonly called partitioning around medoids (PAM). It has the beauty of being basically deterministic and find very good solutions reliably.
k-means is a very simple and ubiquitous clustering algorithm. But quite often it does not work on your problem, for example because the initialization is bad. Fortunately, there is an improved initialization method, k-means++, which can help to alleviate this problem.
Synapses are at the center of neuronal communication, yet they are incredibly difficult to study. During my PhD I created the a comprehensive, quantitative model of the postsynapse at the nanometer scale