Advanced Machine Learning with R

Cluster Analysis

"Quickly bring me a beaker of wine, so that I may wet my mind and say something clever."

- Aristophanes, Athenian Playwright

In the earlier chapters, we focused on trying to learn the best algorithm in order to solve an outcome or response, for example, customer satisfaction or home prices. In all these cases, we had y, and that y is a function of x, or y = f(x). In our data, we had the actual y values and we could train x accordingly. This is referred to as supervised learning. However, there are many situations where we try to learn something from our data, and either we do not have the y, or we actually choose to ignore it. If so, we enter the world of unsupervised learning. In this world, we build and select our algorithm ...