Sum-of-squares clustering on networks

Finding p prototypes by minimizing the sum of the squared distances from a set of points to its closest prototype is a well-studied problem in clustering, data analysis and continuous location. In this note, this very same problem is addressed assuming, for the first time, that the space of possible prototype locations is a network. We develop some interesting properties of such clustering problem. We also show that optimal cluster prototypes are not necessary located at vertices of the network.