Introduction

In this chapter, we will expand on the basic ideas that we built in Chapter 1, Introduction to Clustering, by surrounding clustering with the concept of similarity. Once again, we will be implementing forms of the Euclidean distance to capture the notion of similarity. It is important to bear in mind that the Euclidean distance just happens to be one of the most popular distance metrics; it's not the only one. Through these distance metrics, we will expand on the simple neighbor calculations that we explored in the previous chapter by introducing the concept of hierarchy. By using hierarchy to convey clustering information, we can build stronger groupings that make more logical sense. Similar to k-means, hierarchical clustering can be helpful for cases such as customer segmentation or identifying similar product types. However, there is a slight benefit in being able to explain things in a clearer fashion with hierarchical clustering. In this chapter, we will outline some cases where hierarchical clustering can be the solution you're looking for.