In this section, we will first look at similarity measures. Then, we will learn about hierarchical clustering.
We talked before about different notions of distance in the Computing distances section. Now, I want to talk about the idea of similarity. A similarity score describes how similar two objects are. There is no universal definition of the properties a similarity score has, but everyone agrees that similar objects have a high similarity score and dissimilar objects have a low similarity score. Dissimilarity is the opposite of similarity, and distance is a form of dissimilarity. Hierarchical clustering uses dissimilarity to form clusters. This means that if we can come up with similarity scores that make sense, ...
