Machine Learning with R, the tidyverse, and mlr

Chapter 17. Hierarchical clustering

This chapter covers

• Understanding hierarchical clustering
• Using linkage methods
• Measuring the stability of a clustering result

In the previous chapter, we saw how k-means clustering finds k centroids in the feature space and iteratively updates them to find a set of clusters. Hierarchical clustering takes a different approach and, as its name suggests, can learn a hierarchy of clusters in a dataset. Instead of getting a “flat” output of clusters, hierarchical clustering gives us a tree of clusters within clusters. As a result, hierarchical clustering provides more insight into complex grouping structures than flat clustering methods like k-means.

The tree of clusters is built iteratively by calculating the ...