Hands-On Unsupervised Learning with Python

Agglomerative clustering

As seen in other algorithms, in order to perform aggregations, we need to define a distance metric first, which represents the dissimilarity between samples. We have already analyzed many of them but, in this context, it's helpful to start considering the generic Minkowski distance (parametrized with p):

Two particular cases correspond to p=2 and p=1. In the former case, when p=2, we obtain the standard Euclidean distance (equivalent to the L₂ norm):

When p=1, we obtain the Manhattan or city block distance (equivalent ...