Hands-On Unsupervised Learning with Python

Cluster hierarchies

In the previous chapters, we analyzed clustering algorithms, where the output is a single segmentation based either on a predefined number of clusters or the result of a parameter set and a precise underlying geometry. On the other hand, hierarchical clustering generates a sequence of clustering configurations that can be arranged in the structure of a tree. In particular, let's suppose that we have a dataset, X, containing n samples:

An agglomerative approach starts by assigning each sample to a cluster, C_i, and proceeds by merging two clusters at each step until a single final cluster (corresponding to X) has been produced: ...