Machine Learning Algorithms - Second Edition

Summary

In this chapter, we presented Hierarchical Clustering, focusing our attention on the Agglomerative version, which is the only one supported by scikit-learn. We discussed its philosophy, which is rather different to the one adopted by many other methods. In Agglomerative Clustering, the process begins by considering each sample as a single cluster and proceeds by merging the blocks until the number of desired clusters is reached. To perform this task, two elements are needed: a metric function (also called affinity) and a linkage criterion. The former is used to determine the distance between the elements, while the latter is a target function that is used to determine which clusters must be merged.

We also saw how to visualize this ...