Introduction

So far, we've learned about some of the most basic algorithms of unsupervised learning: k-means clustering and k-medoids clustering. These are not only important for practical use, but are also important for understanding clustering itself.

In this chapter, we're going to study some other advanced clustering algorithms. We aren't calling them advanced because they are difficult to understand, but because, before using them, a data scientist should have insights into why he or she is using these algorithms instead of the general clustering algorithms we studied in the last chapter. k-means is a general-purpose clustering algorithm that is sufficient for most cases, but in some special cases, depending on the type of data, advanced clustering algorithms can produce better results.